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IRJET- SEPD Technique for Removal of Salt and Pepper Noise in Digital ImagesIRJET Journal
This document describes a technique called SEPD (Simple Edge-Preserved Denoising) for removing salt and pepper noise from digital images. SEPD uses a 3x3 pixel window to detect and filter impulse noise while preserving edges. It works by detecting minimum and maximum pixel values (extreme values) in the window, and then uses any directional edges present to estimate the value of the central pixel if it contains impulse noise. The proposed SEPD technique was implemented in VLSI with low computational complexity and memory requirements, making it suitable for real-time embedded applications. Experimental results showed the SEPD technique achieved better image quality than previous methods while using less hardware resources.
The document discusses noise models and methods for removing additive noise from digital images. It describes several types of noise that can affect images, such as Gaussian, impulse, uniform, Rayleigh, gamma and exponential noise. It also presents various noise filters that can be used to remove noise, including mean filters like arithmetic, geometric and harmonic filters, and order statistics filters such as median, max, min and midpoint filters. The filters aim to reduce noise while retaining image detail as much as possible.
DIGITAL VIDEO HOLOGRAPHY- A ROBUST TOOL FOR COMMUNICATIONcscpconf
Holograms are being produced using optical methods for decades. A lot of techniques and
methods exist for the production of efficient holograms. Digital Holography (DH) is
the method of simulating holograms with the use of computer. In this paper digital holograms
are generated using Fresnel and Fraunhofer diffraction integrals. Multi color holograms are
simulated and the digitally generated holograms are analysed. DH technique is extended
further to video format which yields video holograms. The concept that every bit of a hologram
contains full information of the original video, which is being effectively utilized to reduce the
file size required for communication in terms of storage, security and speed. The entire process
is simulated using Matlab7.10 environment.
A HYBRID FILTERING TECHNIQUE FOR ELIMINATING UNIFORM NOISE AND IMPULSE NOIS...sipij
A new hybrid filtering technique is proposed to improving denoising process on digital images.
This technique is performed in two steps. In the first step, uniform noise and impulse noise is
eliminated using decision based algorithm (DBA). Image denoising process is further improved
by an appropriately combining DBA with Adaptive Neuro Fuzzy Inference System (ANFIS) at
the removal of uniform noise and impulse noise on the digital images. Three well known images
are selected for training and the internal parameters of the neuro-fuzzy network are adaptively
optimized by training. This technique offers excellent line, edge, and fine detail preservation
performance while, at the same time, effectively denoising digital images. Extensive simulation
results were realized for ANFIS network and different filters are compared. Results show that
the proposed filter is superior performance in terms of image denoising and edges and fine
details preservation properties.
Homomorphic Filtering of Speckle Noise From Computerized Tomography (CT) Imag...CSCJournals
Adaptive filters are needed to accurately remove noise from noisy images when the variance of noise present varies. Linear filter such as Exponential filter becomes effective in removing speckle noise when homomorphic filtering technique is used. In this paper, an Adaptive Centre- Pixel-Weighed Exponential Filter for removing speckle noise from CT images was developed. The new filter is based on varying the centre-pixel of the filter kernel based on the estimated speckle noise variance present in a noisy CT image. Ten samples of 85x73 CT images corrupted by speckle noise level ranging from 10% to 30% were considered and the new technique gave a reasonably accurate speckle noise filtering performance with an average Peak Signal to Noise Ratio (PSNR) of 70.2839dB compared to 69.0658dB for Wiener filter and 64.3711dB for the Binomial filter. The simulation software used in the paper is Matrix Laboratory (Matlab).
This document discusses image restoration techniques for images degraded by space-variant blurs. It describes running sinusoidal transforms as a method for space-variant image restoration. Running transforms involve applying a short-time orthogonal transform within a moving window, allowing approximately stationary processing. This addresses limitations of methods that assume space-invariance or require coordinate transformations. The chapter presents running discrete sinusoidal transforms as a way to perform the space-variant restoration by modifying orthogonal transform coefficients within the window to estimate pixel values.
Adaptive denoising technique for colour imageseSAT Journals
Abstract
In digital image processing noise removal or noise filtering plays an important role, because for meaningful and useful processing images should not be corrupted by noises. In recent years, high quality televisions have become very popular but noise often affects TV broadcasts. Impulse noise corrupts the video during transmission and acquisition of signals. A number of denoising techniques have been introduced to remove impulse noise from images . Linear noise filtering technique does not work well when the noise is non-adaptive in nature and hence a number of non-linear filtering technique where introduced. In non-linear filtering technique, median filters and its modifications where used to remove noise but it resulted in blurring of images. Therefore here we propose an adaptive digital signal processing approach that can efficiently remove impulse noise from colour image. This algorithm is based on threshold which is adaptive in nature. This algorithm replaces the pixel only if it is found to be noisy pixel otherwise the original pixel is retained thus it results a better filtering technique when compared to median filters and its modified filters.
Keywords: impulse noise, Adaptive threshold, Noise detection, colour video
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
IRJET- SEPD Technique for Removal of Salt and Pepper Noise in Digital ImagesIRJET Journal
This document describes a technique called SEPD (Simple Edge-Preserved Denoising) for removing salt and pepper noise from digital images. SEPD uses a 3x3 pixel window to detect and filter impulse noise while preserving edges. It works by detecting minimum and maximum pixel values (extreme values) in the window, and then uses any directional edges present to estimate the value of the central pixel if it contains impulse noise. The proposed SEPD technique was implemented in VLSI with low computational complexity and memory requirements, making it suitable for real-time embedded applications. Experimental results showed the SEPD technique achieved better image quality than previous methods while using less hardware resources.
The document discusses noise models and methods for removing additive noise from digital images. It describes several types of noise that can affect images, such as Gaussian, impulse, uniform, Rayleigh, gamma and exponential noise. It also presents various noise filters that can be used to remove noise, including mean filters like arithmetic, geometric and harmonic filters, and order statistics filters such as median, max, min and midpoint filters. The filters aim to reduce noise while retaining image detail as much as possible.
DIGITAL VIDEO HOLOGRAPHY- A ROBUST TOOL FOR COMMUNICATIONcscpconf
Holograms are being produced using optical methods for decades. A lot of techniques and
methods exist for the production of efficient holograms. Digital Holography (DH) is
the method of simulating holograms with the use of computer. In this paper digital holograms
are generated using Fresnel and Fraunhofer diffraction integrals. Multi color holograms are
simulated and the digitally generated holograms are analysed. DH technique is extended
further to video format which yields video holograms. The concept that every bit of a hologram
contains full information of the original video, which is being effectively utilized to reduce the
file size required for communication in terms of storage, security and speed. The entire process
is simulated using Matlab7.10 environment.
A HYBRID FILTERING TECHNIQUE FOR ELIMINATING UNIFORM NOISE AND IMPULSE NOIS...sipij
A new hybrid filtering technique is proposed to improving denoising process on digital images.
This technique is performed in two steps. In the first step, uniform noise and impulse noise is
eliminated using decision based algorithm (DBA). Image denoising process is further improved
by an appropriately combining DBA with Adaptive Neuro Fuzzy Inference System (ANFIS) at
the removal of uniform noise and impulse noise on the digital images. Three well known images
are selected for training and the internal parameters of the neuro-fuzzy network are adaptively
optimized by training. This technique offers excellent line, edge, and fine detail preservation
performance while, at the same time, effectively denoising digital images. Extensive simulation
results were realized for ANFIS network and different filters are compared. Results show that
the proposed filter is superior performance in terms of image denoising and edges and fine
details preservation properties.
Homomorphic Filtering of Speckle Noise From Computerized Tomography (CT) Imag...CSCJournals
Adaptive filters are needed to accurately remove noise from noisy images when the variance of noise present varies. Linear filter such as Exponential filter becomes effective in removing speckle noise when homomorphic filtering technique is used. In this paper, an Adaptive Centre- Pixel-Weighed Exponential Filter for removing speckle noise from CT images was developed. The new filter is based on varying the centre-pixel of the filter kernel based on the estimated speckle noise variance present in a noisy CT image. Ten samples of 85x73 CT images corrupted by speckle noise level ranging from 10% to 30% were considered and the new technique gave a reasonably accurate speckle noise filtering performance with an average Peak Signal to Noise Ratio (PSNR) of 70.2839dB compared to 69.0658dB for Wiener filter and 64.3711dB for the Binomial filter. The simulation software used in the paper is Matrix Laboratory (Matlab).
This document discusses image restoration techniques for images degraded by space-variant blurs. It describes running sinusoidal transforms as a method for space-variant image restoration. Running transforms involve applying a short-time orthogonal transform within a moving window, allowing approximately stationary processing. This addresses limitations of methods that assume space-invariance or require coordinate transformations. The chapter presents running discrete sinusoidal transforms as a way to perform the space-variant restoration by modifying orthogonal transform coefficients within the window to estimate pixel values.
Adaptive denoising technique for colour imageseSAT Journals
Abstract
In digital image processing noise removal or noise filtering plays an important role, because for meaningful and useful processing images should not be corrupted by noises. In recent years, high quality televisions have become very popular but noise often affects TV broadcasts. Impulse noise corrupts the video during transmission and acquisition of signals. A number of denoising techniques have been introduced to remove impulse noise from images . Linear noise filtering technique does not work well when the noise is non-adaptive in nature and hence a number of non-linear filtering technique where introduced. In non-linear filtering technique, median filters and its modifications where used to remove noise but it resulted in blurring of images. Therefore here we propose an adaptive digital signal processing approach that can efficiently remove impulse noise from colour image. This algorithm is based on threshold which is adaptive in nature. This algorithm replaces the pixel only if it is found to be noisy pixel otherwise the original pixel is retained thus it results a better filtering technique when compared to median filters and its modified filters.
Keywords: impulse noise, Adaptive threshold, Noise detection, colour video
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
This document summarizes a presentation on wavelet based image compression. It begins with an introduction to image compression, describing why it is needed and common techniques like lossy and lossless compression. It then discusses wavelet transforms and how they are applied to image compression. Several research papers on wavelet compression techniques are reviewed and key advantages like higher compression ratios while maintaining image quality are highlighted. Applications of wavelet compression in areas like biomedicine and multimedia are presented before concluding with references.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document presents a technique called Edge Oriented Denoising Technique (EODT) to remove impulse noise from images. EODT uses a 7-stage pipelined architecture and requires only low computational complexity and two line memory buffers. It detects noisy pixels using an extreme data detector and estimated corrected values using an edge-oriented noise filter. A VLSI implementation of EODT is also presented, which achieves better image quality than previous methods with lower hardware costs. Simulation results show the technique can correctly identify and remove impulse noise even when noise ratios are as high as 90%.
Computational scrutiny of image denoising method found on DBAMF under SPN sur...IJECEIAES
Traditionally, rank order absolute difference (ROAD) has a great similarity capacity for identifying whether the pixel is SPN or noiseless because statistical characteristic of ROAD is desired for a noise identifying objective. As a result, the decision based adaptive median filter (DBAMF) that is found on ROAD technique has been initially proposed for eliminating an impulsive noise since 2010. Consequently, this analyzed report focuses to examine the similarity capacity of denoising method found on DBAMF for diverse SPN Surrounding. In order to examine the denoising capacity and its obstruction of the denoising method found on DBAMF, the four original digital images, comprised of Airplane, Pepper, Girl and Lena, are examined in these computational simulations for SPN surrounding by initially contaminating the SPN with diverse intensity. Later, all contaminated digital images are denoised by the denoising method found on DBAMF. In addition, the proposed denoised image, which is computed by this DBAMF denoising method, is confronted with the other denoised images, which is computed by standard median filter (SMF), gaussian filter and adaptive median filter (AMF) for demonstrating the DBAMF capacity under subjective measurement aspect.
All optical image processing using third harmonic generation for image correl...M. Faisal Halim
Term Paper: All optical image processing using third harmonic generation for image correlation
Optical Information Processing Course
Monday, 20th December, 2010
This document describes an image denoising technique called the TWIST (Transform With Iterative Sampling and Thresholding) method. It begins with background on common types of image noise like Gaussian, salt-and-pepper, and quantization noise. It then discusses related work using eigendecomposition and the Nystrom extension for denoising. The proposed TWIST method uses the Nystrom extension to approximate the filter matrix with a low-rank matrix, allowing efficient processing of the entire image. It performs eigendecomposition on sample pixels to estimate eigenvalues and eigenvectors, then iterates this process with thresholding to denoise the image while preserving edges.
Adaptive Noise Reduction Scheme for Salt and Peppersipij
In this paper, a new adaptive noise reduction scheme for images corrupted by impulse noise is presented. The proposed scheme efficiently identifies and reduces salt and pepper noise. MAG (Mean Absolute Gradient) is used to identify pixels which are most likely corrupted by salt and pepper noise that are candidates for further median based noise reduction processing. Directional filtering is then applied after noise reduction to achieve a good tradeoff between detail preservation and noise removal. The proposed scheme can remove salt and pepper noise with noise density as high as 90% and produce better result in terms of qualitative and quantitative measures of images.
CORRELATION BASED FUNDAMENTAL FREQUENCY EXTRACTION METHOD IN NOISY SPEECH SIGNALijcseit
This paper proposed a correlation based method using the autocorrelation function and the YIN. The
autocorrelation function and also YIN is a popular measurement in estimating fundamental frequency in
time domain. The performance of these two methods, however, is effected due to the position of dominant
harmonics (usually the first formant) and the presence of spurious peaks introduced in noisy conditions.
The experimental results of computer simulations on female and male voices in different noises perform
that the gross pitch errors are lower in proposed method as compared to other related method in different
types of signal to noise ratio conditions.
The document proposes a new noise removal technique called the Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The MDBUTMF first detects salt and pepper noise pixels before filtering. It then classifies each pixel as either noisy or noise-free. Noise-free pixels are left unchanged, while noisy pixels are processed depending on their neighbors: if all neighbors are noisy, the pixel is replaced with the mean; otherwise, noisy neighbors are eliminated and the pixel is replaced with the median. The algorithm aims to remove noise while preserving details better than existing methods. It processes each image pixel with this classification and filtering approach to reduce salt and pepper noise from corrupted images.
Various Applications of Compressive Sensing in Digital Image Processing: A Su...IRJET Journal
1. The document discusses various applications of compressive sensing in digital image processing. It describes how compressive sensing has been used successfully in areas like video encoding, atomic force microscopy imaging, image encryption, and reconstructing missing areas in images.
2. Compressive sensing allows reconstruction of images from incomplete information using fewer measurements than traditional methods. It takes advantage of sparsity and redundancy in images.
3. The applications discussed show that compressive sensing provides better results than other methods for tasks like video encoding, microscopy imaging, encryption, and filling in missing areas due to clouds in images.
The document discusses superresolution technology that can improve the resolution of infrared camera images. It begins by explaining the basic problem that small objects may be invisible or measured incorrectly in infrared images due to pixel size limitations. It then describes how superresolution works by using multiple images and deconvolution algorithms to effectively decrease pixel pitch by 1.6x and increase usable resolution also by 1.6x compared to normal images. Experimental results show that superresolution detects spatial frequencies about 50% higher than the camera's detector cutoff and improves temperature measurement accuracy compared to interpolation. The technology will be available as a software update for all current Testo infrared cameras.
A Decision tree and Conditional Median Filter Based Denoising for impulse noi...IJERA Editor
Impulse noise is often introduced into images during acquisition and transmission. Even though so many denoising techniques are existing for the removal of impulse noise in images, most of them are high complexity methods and have only low image quality. Here a low cost, low complexity VLSI architecture for the removal of random valued impulse noise in highly corrupted images is introduced. In this technique a decision- tree- based impulse noise detector is used to detect the noisy pixels and an efficient conditional median filter is used to reconstruct the intensity values of noisy pixels. The proposed technique can improve the signal to noise ratio than any other technique.
Digital Image Processing: Image Enhancement in the Frequency DomainMostafa G. M. Mostafa
This document is a chapter from a textbook on digital image processing. It discusses the discrete Fourier transform (DFT) and its properties. It also covers various filtering techniques that can be performed in the frequency domain, including low-pass, high-pass, band-pass, and homomorphic filters using approaches like Gaussian, Butterworth, and ideal filters. Homework problems 4.9 and 4.12 are also mentioned at the end.
Use of Discrete Sine Transform for A Novel Image Denoising TechniqueCSCJournals
In this paper, we propose a new multiresolution image denoising technique using Discrete Sine Transform. Wavelet techniques have been in use for multiresolution image processing. Discrete Cosine Transform is also extensively used for image compression. Similar to the Discrete Wavelet and Discrete Cosine Transform it is now found that Discrete Sine Transform also possess some good qualities for image processing; specifically for image denoising. Algorithm for image denoising using Discrete Sine Transform is proposed with simulation works for experimental verification. The method is computationally efficient and simple in theory and application.
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
1) The document proposes a method for tracking moving objects in videos captured using a moving camera in complex scenes. It involves video stabilization, key frame extraction, object detection/tracking using Gaussian mixture models and Kalman filters, and object recognition using bag of features.
2) Key frame extraction identifies important frames for processing by computing edge differences between frames and selecting frames above a threshold.
3) Moving objects are detected using background subtraction and Gaussian mixture models, and then tracked across frames using Kalman filters.
4) Object recognition is performed using bag of features, which represents objects as histograms of visual word frequencies to classify objects based on characteristic visual parts.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Optimized Implementation of Edge Preserving Color Guided Filter for Video on ...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
Digital image processing - Image Enhancement (MATERIAL)Mathankumar S
This document discusses various image enhancement techniques including contrast stretching, compression of dynamic range, histogram equalization, and histogram specification. It provides definitions and explanations of these concepts with examples. Histogram equalization aims to produce a linear histogram to enhance an image, while histogram specification allows specifying a desired output histogram. Local enhancement can be achieved by applying these histogram processing methods over small non-overlapping regions instead of globally to reduce edge effects.
Stereo Vision Human Motion Detection and Tracking in Uncontrolled EnvironmentTELKOMNIKA JOURNAL
Stereo vision in detecting human motion is an emerging research for automation, robotics, and sports science field due to the advancement of imaging sensors and information technology. The difficulty of human motion detection and tracking is relatively complex when it is applied to uncontrolled environment. In this paper, a hybrid filter approach is proposed to detect human motion in the stereo vision. The hybrid filter approach integrates Gaussian filter and median filter to reduce the coverage of shadow and sudden change of illumination. In addition, sequential thinning and thickening morphological method is used to construct the skeleton model. The proposed hybrid approach is compared with the normalized filter. As a result, the proposed approach produces better skeleton model with less influential effect on shadow and illumination. The output results of the proposed approach can show up to 86% of average accuracy matched with skeleton model. In addition, obtains approximately 94% of sensitivity measurement in the stereo vision. The proposed approach using hybrid filter and sequential morphology could improve the performance of the detection in the uncontrolled environment.
noise remove in image processing by fuzzy logicRucku
This document summarizes a research paper that proposes a two-stage technique for removing impulse noise from digital images using neural networks and fuzzy logic. In the first stage, a neural network is used to detect and remove noise while preserving image details. In the second stage, fuzzy decision rules inspired by the human visual system are used to further process pixels and enhance image quality, especially in sensitive regions. The technique aims to remove noise cleanly without blurring edges or destroying important information. It is presented as an improvement over conventional noise removal methods.
Filter for Removal of Impulse Noise By Using Fuzzy LogicCSCJournals
Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing [4] is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. This paper combines the features of Image Enhancement and fuzzy logic. This research problem deals with Fuzzy inference system (FIS) which help to take the decision about the pixels of the image under consideration. This paper focuses on the removal of the impulse noise with the preservation of edge sharpness and image details along with improving the contrast of the images which is considered as the one of the most difficult tasks in image processing.
1) The document discusses a technique for detecting bone fractures in x-ray images using edge detection methods like Gaussian and Canny edge detection.
2) It involves preprocessing the x-ray image, applying Gaussian filtering to remove noise, using Canny edge detection to identify edges, and inverting the image to make fractures more visible.
3) The method is implemented using the AForge library and is found to accurately detect bone fractures in x-ray images for use in medical applications like aiding doctors' diagnoses.
This document summarizes a presentation on wavelet based image compression. It begins with an introduction to image compression, describing why it is needed and common techniques like lossy and lossless compression. It then discusses wavelet transforms and how they are applied to image compression. Several research papers on wavelet compression techniques are reviewed and key advantages like higher compression ratios while maintaining image quality are highlighted. Applications of wavelet compression in areas like biomedicine and multimedia are presented before concluding with references.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document presents a technique called Edge Oriented Denoising Technique (EODT) to remove impulse noise from images. EODT uses a 7-stage pipelined architecture and requires only low computational complexity and two line memory buffers. It detects noisy pixels using an extreme data detector and estimated corrected values using an edge-oriented noise filter. A VLSI implementation of EODT is also presented, which achieves better image quality than previous methods with lower hardware costs. Simulation results show the technique can correctly identify and remove impulse noise even when noise ratios are as high as 90%.
Computational scrutiny of image denoising method found on DBAMF under SPN sur...IJECEIAES
Traditionally, rank order absolute difference (ROAD) has a great similarity capacity for identifying whether the pixel is SPN or noiseless because statistical characteristic of ROAD is desired for a noise identifying objective. As a result, the decision based adaptive median filter (DBAMF) that is found on ROAD technique has been initially proposed for eliminating an impulsive noise since 2010. Consequently, this analyzed report focuses to examine the similarity capacity of denoising method found on DBAMF for diverse SPN Surrounding. In order to examine the denoising capacity and its obstruction of the denoising method found on DBAMF, the four original digital images, comprised of Airplane, Pepper, Girl and Lena, are examined in these computational simulations for SPN surrounding by initially contaminating the SPN with diverse intensity. Later, all contaminated digital images are denoised by the denoising method found on DBAMF. In addition, the proposed denoised image, which is computed by this DBAMF denoising method, is confronted with the other denoised images, which is computed by standard median filter (SMF), gaussian filter and adaptive median filter (AMF) for demonstrating the DBAMF capacity under subjective measurement aspect.
All optical image processing using third harmonic generation for image correl...M. Faisal Halim
Term Paper: All optical image processing using third harmonic generation for image correlation
Optical Information Processing Course
Monday, 20th December, 2010
This document describes an image denoising technique called the TWIST (Transform With Iterative Sampling and Thresholding) method. It begins with background on common types of image noise like Gaussian, salt-and-pepper, and quantization noise. It then discusses related work using eigendecomposition and the Nystrom extension for denoising. The proposed TWIST method uses the Nystrom extension to approximate the filter matrix with a low-rank matrix, allowing efficient processing of the entire image. It performs eigendecomposition on sample pixels to estimate eigenvalues and eigenvectors, then iterates this process with thresholding to denoise the image while preserving edges.
Adaptive Noise Reduction Scheme for Salt and Peppersipij
In this paper, a new adaptive noise reduction scheme for images corrupted by impulse noise is presented. The proposed scheme efficiently identifies and reduces salt and pepper noise. MAG (Mean Absolute Gradient) is used to identify pixels which are most likely corrupted by salt and pepper noise that are candidates for further median based noise reduction processing. Directional filtering is then applied after noise reduction to achieve a good tradeoff between detail preservation and noise removal. The proposed scheme can remove salt and pepper noise with noise density as high as 90% and produce better result in terms of qualitative and quantitative measures of images.
CORRELATION BASED FUNDAMENTAL FREQUENCY EXTRACTION METHOD IN NOISY SPEECH SIGNALijcseit
This paper proposed a correlation based method using the autocorrelation function and the YIN. The
autocorrelation function and also YIN is a popular measurement in estimating fundamental frequency in
time domain. The performance of these two methods, however, is effected due to the position of dominant
harmonics (usually the first formant) and the presence of spurious peaks introduced in noisy conditions.
The experimental results of computer simulations on female and male voices in different noises perform
that the gross pitch errors are lower in proposed method as compared to other related method in different
types of signal to noise ratio conditions.
The document proposes a new noise removal technique called the Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The MDBUTMF first detects salt and pepper noise pixels before filtering. It then classifies each pixel as either noisy or noise-free. Noise-free pixels are left unchanged, while noisy pixels are processed depending on their neighbors: if all neighbors are noisy, the pixel is replaced with the mean; otherwise, noisy neighbors are eliminated and the pixel is replaced with the median. The algorithm aims to remove noise while preserving details better than existing methods. It processes each image pixel with this classification and filtering approach to reduce salt and pepper noise from corrupted images.
Various Applications of Compressive Sensing in Digital Image Processing: A Su...IRJET Journal
1. The document discusses various applications of compressive sensing in digital image processing. It describes how compressive sensing has been used successfully in areas like video encoding, atomic force microscopy imaging, image encryption, and reconstructing missing areas in images.
2. Compressive sensing allows reconstruction of images from incomplete information using fewer measurements than traditional methods. It takes advantage of sparsity and redundancy in images.
3. The applications discussed show that compressive sensing provides better results than other methods for tasks like video encoding, microscopy imaging, encryption, and filling in missing areas due to clouds in images.
The document discusses superresolution technology that can improve the resolution of infrared camera images. It begins by explaining the basic problem that small objects may be invisible or measured incorrectly in infrared images due to pixel size limitations. It then describes how superresolution works by using multiple images and deconvolution algorithms to effectively decrease pixel pitch by 1.6x and increase usable resolution also by 1.6x compared to normal images. Experimental results show that superresolution detects spatial frequencies about 50% higher than the camera's detector cutoff and improves temperature measurement accuracy compared to interpolation. The technology will be available as a software update for all current Testo infrared cameras.
A Decision tree and Conditional Median Filter Based Denoising for impulse noi...IJERA Editor
Impulse noise is often introduced into images during acquisition and transmission. Even though so many denoising techniques are existing for the removal of impulse noise in images, most of them are high complexity methods and have only low image quality. Here a low cost, low complexity VLSI architecture for the removal of random valued impulse noise in highly corrupted images is introduced. In this technique a decision- tree- based impulse noise detector is used to detect the noisy pixels and an efficient conditional median filter is used to reconstruct the intensity values of noisy pixels. The proposed technique can improve the signal to noise ratio than any other technique.
Digital Image Processing: Image Enhancement in the Frequency DomainMostafa G. M. Mostafa
This document is a chapter from a textbook on digital image processing. It discusses the discrete Fourier transform (DFT) and its properties. It also covers various filtering techniques that can be performed in the frequency domain, including low-pass, high-pass, band-pass, and homomorphic filters using approaches like Gaussian, Butterworth, and ideal filters. Homework problems 4.9 and 4.12 are also mentioned at the end.
Use of Discrete Sine Transform for A Novel Image Denoising TechniqueCSCJournals
In this paper, we propose a new multiresolution image denoising technique using Discrete Sine Transform. Wavelet techniques have been in use for multiresolution image processing. Discrete Cosine Transform is also extensively used for image compression. Similar to the Discrete Wavelet and Discrete Cosine Transform it is now found that Discrete Sine Transform also possess some good qualities for image processing; specifically for image denoising. Algorithm for image denoising using Discrete Sine Transform is proposed with simulation works for experimental verification. The method is computationally efficient and simple in theory and application.
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
1) The document proposes a method for tracking moving objects in videos captured using a moving camera in complex scenes. It involves video stabilization, key frame extraction, object detection/tracking using Gaussian mixture models and Kalman filters, and object recognition using bag of features.
2) Key frame extraction identifies important frames for processing by computing edge differences between frames and selecting frames above a threshold.
3) Moving objects are detected using background subtraction and Gaussian mixture models, and then tracked across frames using Kalman filters.
4) Object recognition is performed using bag of features, which represents objects as histograms of visual word frequencies to classify objects based on characteristic visual parts.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Optimized Implementation of Edge Preserving Color Guided Filter for Video on ...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
Digital image processing - Image Enhancement (MATERIAL)Mathankumar S
This document discusses various image enhancement techniques including contrast stretching, compression of dynamic range, histogram equalization, and histogram specification. It provides definitions and explanations of these concepts with examples. Histogram equalization aims to produce a linear histogram to enhance an image, while histogram specification allows specifying a desired output histogram. Local enhancement can be achieved by applying these histogram processing methods over small non-overlapping regions instead of globally to reduce edge effects.
Stereo Vision Human Motion Detection and Tracking in Uncontrolled EnvironmentTELKOMNIKA JOURNAL
Stereo vision in detecting human motion is an emerging research for automation, robotics, and sports science field due to the advancement of imaging sensors and information technology. The difficulty of human motion detection and tracking is relatively complex when it is applied to uncontrolled environment. In this paper, a hybrid filter approach is proposed to detect human motion in the stereo vision. The hybrid filter approach integrates Gaussian filter and median filter to reduce the coverage of shadow and sudden change of illumination. In addition, sequential thinning and thickening morphological method is used to construct the skeleton model. The proposed hybrid approach is compared with the normalized filter. As a result, the proposed approach produces better skeleton model with less influential effect on shadow and illumination. The output results of the proposed approach can show up to 86% of average accuracy matched with skeleton model. In addition, obtains approximately 94% of sensitivity measurement in the stereo vision. The proposed approach using hybrid filter and sequential morphology could improve the performance of the detection in the uncontrolled environment.
noise remove in image processing by fuzzy logicRucku
This document summarizes a research paper that proposes a two-stage technique for removing impulse noise from digital images using neural networks and fuzzy logic. In the first stage, a neural network is used to detect and remove noise while preserving image details. In the second stage, fuzzy decision rules inspired by the human visual system are used to further process pixels and enhance image quality, especially in sensitive regions. The technique aims to remove noise cleanly without blurring edges or destroying important information. It is presented as an improvement over conventional noise removal methods.
Filter for Removal of Impulse Noise By Using Fuzzy LogicCSCJournals
Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing [4] is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. This paper combines the features of Image Enhancement and fuzzy logic. This research problem deals with Fuzzy inference system (FIS) which help to take the decision about the pixels of the image under consideration. This paper focuses on the removal of the impulse noise with the preservation of edge sharpness and image details along with improving the contrast of the images which is considered as the one of the most difficult tasks in image processing.
1) The document discusses a technique for detecting bone fractures in x-ray images using edge detection methods like Gaussian and Canny edge detection.
2) It involves preprocessing the x-ray image, applying Gaussian filtering to remove noise, using Canny edge detection to identify edges, and inverting the image to make fractures more visible.
3) The method is implemented using the AForge library and is found to accurately detect bone fractures in x-ray images for use in medical applications like aiding doctors' diagnoses.
Fuzzy image processing uses fuzzy logic techniques to process digital images. It can handle vagueness and ambiguity in images. The main steps are image fuzzification, modifying membership values, and image defuzzification. Fuzzy image processing has applications in noise removal, edge detection, segmentation, and contrast enhancement. It provides advantages over traditional techniques by allowing for graded membership in sets rather than binary membership.
This document provides an overview of fuzzy logic and fuzzy set theory with examples from image processing. Some key points:
- Fuzzy set theory was coined by Lofti Zadeh in 1965 and allows for degrees of membership rather than binary true/false values. Almost all real-world classes are fuzzy.
- Fuzzy logic handles imprecise concepts like "tall person" through membership functions and handles inferences through generalized modus ponens.
- Fuzzy logic has been applied to fields like image processing, where concepts like "light blue" are fuzzy, and speech recognition by assigning fuzzy values to phonemes.
- Techniques discussed include fuzzy membership functions, aggregation operations, alpha cuts, linguistic
Number plate recognition system using matlab.Namra Afzal
The document describes a student project to develop a car recognition system using MATLAB. The system aims to detect and recognize car number plates using image processing and optical character recognition algorithms. A group of three students divided the work, with one student writing the Matlab code, another interfacing the system with a microcontroller, and the third building the hardware. The document outlines the workflow and basic modules of the system, including license plate localization, character segmentation, and character recognition using template matching in Matlab. It also discusses some problems faced with the Matlab-based system.
This document discusses noise addition and filtering in images. It begins by introducing different types of digital images like binary, grayscale, and color images. It then discusses various sources of image noise like sensor heat, ISO settings, and memory failures. The main types of noise covered are salt and pepper noise, Gaussian noise, speckle noise, and uniform noise. Linear and non-linear filtering techniques are described for removing each noise type, including median filtering, Wiener filtering, and mean/Gaussian filtering. Performance of filters is evaluated using measures like mean squared error and peak signal-to-noise ratio. Matlab is mentioned for implementing noise addition and filtering.
This document describes a computer vision approach to audio enhancement by removing unwanted noises from recordings. The approach uses object detection techniques to detect noises in spectrograms of audio clips. The user mimics the unwanted noise, which is then detected as an "object" in the spectrogram using HOG features and classification. Multiple techniques are evaluated for scanning, feature extraction, classification and detecting multiple objects. Results show the approach can effectively remove noises, though may struggle with similar noises or incomplete detections.
Strengthen Fuzzy Pronouncement for Impulse Noise Riddance Method for Images B...IRJET Journal
This document proposes a 4-stage neural network method for removing impulse noise from images. It begins with a 1st stage additive neural network to cleanly remove noise while preserving uncorrupted data. The 2nd stage uses fuzzy decision rules inspired by the human visual system to compensate for blurring and details lost from median filtering. A 3rd neural network is then used to enhance regions determined by the fuzzy rules to have higher visual importance. The goal is to remove impulse noise cleanly without blurring edges, by dividing the method into noise removal and subsequent image enhancement stages using neural networks and fuzzy logic.
This document summarizes an article that proposes an adaptive nonlinear filtering technique for image restoration. It begins by discussing common types of image noise and degradation models. It then discusses existing median filtering and adaptive filtering techniques that aim to remove noise while preserving edges. The paper proposes a new adaptive length median/mean algorithm that can simultaneously remove noise artifacts like impulses, strip lines, drop lines, band missing, and blotches. It detects corrupted pixels and evaluates new pixels to replace them. The algorithm switches between median and mean filtering depending on noise levels to better preserve details. The performance of the algorithm is evaluated based on metrics like mean square error and peak signal-to-noise ratio. The algorithm is found to outperform standard techniques in
Development and Implementation of VLSI Reconfigurable Architecture for Gabor ...Dr. Amarjeet Singh
This document presents a development of a VLSI reconfigurable architecture for a Gabor filter to be used in medical image applications, specifically for tonsillitis detection. It first provides background on Gabor filtering and its use in applications like texture analysis, object recognition, and medical image processing. It then reviews related works that have implemented Gabor filters. The document goes on to describe the proposed tonsillitis detection system, which includes modules for preprocessing, CORDIC filtering, filter generation, and convolution. It discusses simulating and synthesizing the design in Verilog and FPGA implementation. The results showed the design could operate at 394.563 MHz on an Artix 7 board.
An Adaptive approach to retrieve image affected by impulse noise from documentsiosrjce
This document discusses techniques for removing impulse noise from images. It begins by defining impulse noise and how it appears as random white or black pixels in an image. It then discusses several filtering techniques for noise removal, including mean, median, and adaptive filtering. Mean filtering techniques calculate the average pixel value in a neighborhood, but can blur edges. Median filtering replaces pixels with the median value in a neighborhood, which is better at removing salt and pepper noise while preserving edges. Adaptive filtering uses local statistics to determine whether to apply mean or median filtering, improving noise removal while minimizing blurring. The document concludes adaptive median filtering is very useful for removing impulse noise from images.
This document discusses impulse noise in images and techniques for removing it. It begins by defining impulse or salt-and-pepper noise as randomly scattered white or black pixels that occur due to transmission errors or sensor issues. Common filtering techniques for removal are then described, including mean, median, and adaptive filtering. Mean filtering calculates the average pixel value in a neighborhood but can lose image quality. Median filtering replaces pixels with the middle value in a neighborhood, making it better at removing salt-and-pepper noise while preserving image details. Adaptive filters vary their behavior based on pixel values to act more like mean or median filters in different areas.
FPGA Implementation of Decision Based Algorithm for Removal of Impulse NoiseIRJET Journal
This document proposes implementing a decision-based algorithm for removing impulse noise from images using an FPGA. It summarizes the algorithm, which detects and filters impulse noise in images by checking pixel values within a window. The algorithm replaces noisy pixel values with either the median or mean of pixel values in the window. The document outlines the architecture for implementing this algorithm on an FPGA, which detects noise, filters noise by calculating median/mean values, and stores output in memory. It reviews related work on impulse noise removal and non-linear filtering, noting advantages of the decision-based algorithm and FPGA implementation for image processing applications.
Parameterized Image Filtering Using fuzzy LogicEditor IJCATR
The principal source of blur in digital images arise during image acquisition (digitization) or transmission. The
performance of imaging sensors is affected by a variety of factors, such as the environmental conditions during image acquisition.
Blurry images are the result of movement of the camera during shooting (not holding it still) or the camera not being capable of
choosing a fast enough shutter speed to freeze the action under the light conditions. For instance, in acquiring images with a camera,
light levels and sensor temperature are major factors affecting the amount of blur in the resulting image.
Blur was implemented by first creating a PSF filter in MatLab that would approximate linear motion blur. This PSF was then
convolved with the original image to produce the blurred image. Convolution is a mathematical process by which a signal, in this case
the image, is acted on by a system, the filter, in order to find the resulting signal. The amount of blur added to the original image
depended on two parameters of the PSF: length of blur (in pixels), and the angle of the blur. This thesis work is going to provide a
new, faster, and more efficient noise reduction method for images corrupted with motion blur. This new filter has two separated steps
or phases: the detection phase and the filtering phase. The detection phase uses fuzzy rules to determine whether a image is blurred or
not. When blurry image is detected, Then we use fuzzy filtering technique focuses only on the on the real blurred pixels.
A Hybrid Filtering Technique for Random Valued Impulse Noise Elimination on D...IDES Editor
This document summarizes a research paper that proposes a hybrid filtering technique combining an Asymmetric Trimmed Median Filter (ATMF) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) to remove random valued impulse noise from digital images. The technique performs noise removal in two steps: first using ATMF, then combining the ATMF output with the original noisy image as inputs to an ANFIS network to further refine the image. The ANFIS network is trained on three test images to optimize its parameters for improved noise removal while preserving edges and details. Simulation results showed the proposed hybrid filter performed better than other filters in terms of image denoising and detail preservation.
Noise Reduction Technique using Bilateral Based FilterIRJET Journal
This document proposes a noise removal technique using a bilateral filter based on spatial gradients and minimum mean square error (MMSE) filtering. It consists of two steps: 1) A reference image is generated from the noisy image by applying a spatial gradient-based bilateral filter to 3x3 patches, and 2) An MMSE filter is applied to the reference image to reduce the mean square error. Generally, noise removal techniques alter the natural appearance of images, but this method aims to restore the image without affecting its natural appearance. The technique is evaluated using metrics like PSNR to compare its performance to other filters.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
Satellite image compression algorithm based on the fftijma
Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the
image to an unacceptable level ,the reduction in file size allows more images to be stored in a given amount
of disk or memory space, it also reduces the time required for images to be sent over the ground This paper
presents a new coding scheme for satellite images. In this study we apply the fast Fourier transform and the
scalar quantization for standard LENA image and satellite image, The results obtained after the (SQ) phase
are encoded using entropy encoding, after decompression, the results show that it is possible to achieve
higher compression ratios, more than 78%, the results are discussed in the paper.
Satellite Image Compression Algorithm Based on the FFTijma
Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the
image to an unacceptable level ,the reduction in file size allows more images to be stored in a given amount
of disk or memory space, it also reduces the time required for images to be sent over the ground This paper
presents a new coding scheme for satellite images. In this study we apply the fast Fourier transform and the
scalar quantization for standard LENA image and satellite image, The results obtained after the (SQ) phase
are encoded using entropy encoding, after decompression, the results show that it is possible to achieve
higher compression ratios, more than 78%, the results are discussed in the paper.
This document summarizes research on using different filters to reduce noise in digital images. It begins by introducing digital image processing and its advantages over analog processing. Then it discusses three types of filters:
1) Mean filter, which replaces each pixel value with the average of neighboring pixels, reducing local variations caused by noise.
2) Median filter, a nonlinear filter that replaces each pixel with the median of neighboring pixels, preserving edges while removing noise.
3) Wiener filter, an optimal linear filter that minimizes the mean square error between the estimated image and the original. It operates by weighting each pixel by the power spectral density of the noise and original image.
The document then evaluates these filters for removing
This document describes a two-stage technique for removing impulse noise from digital images using neural networks and fuzzy logic. In the first stage, a neural network is used to detect and remove noise from the image cleanly while preserving image details. In the second stage, fuzzy decision rules inspired by the human visual system are used to enhance image quality by compensating for blurring or destruction caused in the first stage. The goal is to remove noise cleanly without blurring edges while enhancing the overall visual quality of the processed image.
A GAUSSIAN MIXTURE MODEL BASED SPEECH RECOGNITION SYSTEM USING MATLABsipij
1) The document describes the development of a speaker-dependent speech recognition system using MATLAB. It uses Gaussian mixture models for acoustic modeling and mel-frequency cepstral coefficients for feature extraction.
2) The system is designed to recognize isolated digits 0-9. Voice activity detection is performed to detect segments of speech. Various windowing functions are evaluated to reduce spectral leakage during feature extraction.
3) 13 MFCCs plus energy are extracted from each 30ms frame with 10ms shift. The first and second derivatives are also calculated to capture dynamic information, resulting in a 39-dimensional feature vector.
A Comparative Study of Image Denoising Techniques for Medical ImagesIRJET Journal
This document discusses image denoising techniques for medical images. It begins by introducing how medical images are used for disease diagnosis but the image acquisition process can introduce noise. The goal of image denoising is to remove noise while preserving image details. Different types of noise that affect medical images are described such as Gaussian, salt and pepper, and speckle noise. Denoising techniques are categorized as operating in the spatial domain using filters like mean, median, and adaptive median filters, or in the transform domain using wavelet thresholding. Performance is measured using metrics like peak signal-to-noise ratio and mean squared error. In conclusion, transform domain filtering with wavelets is effective due to properties like sparsity and multi-
A review of Noise Suppression Technology for Real-Time Speech EnhancementIRJET Journal
This document summarizes research on noise suppression technology for real-time speech enhancement. It discusses how noise suppression has gained interest due to advances in deep learning techniques. It describes how noise suppression works by using multiple microphones to capture audio signals, which are then processed using algorithms to separate and suppress background noises while enhancing speech. Deep learning has achieved promising results for noise suppression by training models to detect human voice between different input noises. The document also reviews conventional uses of noise suppression in devices and limitations, and how using deep learning allows for more effective separation of noise from sound signals.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to International Journal of Engineering Research and Development (IJERD) (20)
A Novel Method for Prevention of Bandwidth Distributed Denial of Service AttacksIJERD Editor
Distributed Denial of Service (DDoS) Attacks became a massive threat to the Internet. Traditional
Architecture of internet is vulnerable to the attacks like DDoS. Attacker primarily acquire his army of Zombies,
then that army will be instructed by the Attacker that when to start an attack and on whom the attack should be
done. In this paper, different techniques which are used to perform DDoS Attacks, Tools that were used to
perform Attacks and Countermeasures in order to detect the attackers and eliminate the Bandwidth Distributed
Denial of Service attacks (B-DDoS) are reviewed. DDoS Attacks were done by using various Flooding
techniques which are used in DDoS attack.
The main purpose of this paper is to design an architecture which can reduce the Bandwidth
Distributed Denial of service Attack and make the victim site or server available for the normal users by
eliminating the zombie machines. Our Primary focus of this paper is to dispute how normal machines are
turning into zombies (Bots), how attack is been initiated, DDoS attack procedure and how an organization can
save their server from being a DDoS victim. In order to present this we implemented a simulated environment
with Cisco switches, Routers, Firewall, some virtual machines and some Attack tools to display a real DDoS
attack. By using Time scheduling, Resource Limiting, System log, Access Control List and some Modular
policy Framework we stopped the attack and identified the Attacker (Bot) machines
Hearing loss is one of the most common human impairments. It is estimated that by year 2015 more
than 700 million people will suffer mild deafness. Most can be helped by hearing aid devices depending on the
severity of their hearing loss. This paper describes the implementation and characterization details of a dual
channel transmitter front end (TFE) for digital hearing aid (DHA) applications that use novel micro
electromechanical- systems (MEMS) audio transducers and ultra-low power-scalable analog-to-digital
converters (ADCs), which enable a very-low form factor, energy-efficient implementation for next-generation
DHA. The contribution of the design is the implementation of the dual channel MEMS microphones and powerscalable
ADC system.
Influence of tensile behaviour of slab on the structural Behaviour of shear c...IJERD Editor
-A composite beam is composed of a steel beam and a slab connected by means of shear connectors
like studs installed on the top flange of the steel beam to form a structure behaving monolithically. This study
analyzes the effects of the tensile behavior of the slab on the structural behavior of the shear connection like slip
stiffness and maximum shear force in composite beams subjected to hogging moment. The results show that the
shear studs located in the crack-concentration zones due to large hogging moments sustain significantly smaller
shear force and slip stiffness than the other zones. Moreover, the reduction of the slip stiffness in the shear
connection appears also to be closely related to the change in the tensile strain of rebar according to the increase
of the load. Further experimental and analytical studies shall be conducted considering variables such as the
reinforcement ratio and the arrangement of shear connectors to achieve efficient design of the shear connection
in composite beams subjected to hogging moment.
Gold prospecting using Remote Sensing ‘A case study of Sudan’IJERD Editor
Gold has been extracted from northeast Africa for more than 5000 years, and this may be the first
place where the metal was extracted. The Arabian-Nubian Shield (ANS) is an exposure of Precambrian
crystalline rocks on the flanks of the Red Sea. The crystalline rocks are mostly Neoproterozoic in age. ANS
includes the nations of Israel, Jordan. Egypt, Saudi Arabia, Sudan, Eritrea, Ethiopia, Yemen, and Somalia.
Arabian Nubian Shield Consists of juvenile continental crest that formed between 900 550 Ma, when intra
oceanic arc welded together along ophiolite decorated arc. Primary Au mineralization probably developed in
association with the growth of intra oceanic arc and evolution of back arc. Multiple episodes of deformation
have obscured the primary metallogenic setting, but at least some of the deposits preserve evidence that they
originate as sea floor massive sulphide deposits.
The Red Sea Hills Region is a vast span of rugged, harsh and inhospitable sector of the Earth with
inimical moon-like terrain, nevertheless since ancient times it is famed to be an abode of gold and was a major
source of wealth for the Pharaohs of ancient Egypt. The Pharaohs old workings have been periodically
rediscovered through time. Recent endeavours by the Geological Research Authority of Sudan led to the
discovery of a score of occurrences with gold and massive sulphide mineralizations. In the nineties of the
previous century the Geological Research Authority of Sudan (GRAS) in cooperation with BRGM utilized
satellite data of Landsat TM using spectral ratio technique to map possible mineralized zones in the Red Sea
Hills of Sudan. The outcome of the study mapped a gossan type gold mineralization. Band ratio technique was
applied to Arbaat area and a signature of alteration zone was detected. The alteration zones are commonly
associated with mineralization. The alteration zones are commonly associated with mineralization. A filed check
confirmed the existence of stock work of gold bearing quartz in the alteration zone. Another type of gold
mineralization that was discovered using remote sensing is the gold associated with metachert in the Atmur
Desert.
Reducing Corrosion Rate by Welding DesignIJERD Editor
This document summarizes a study on reducing corrosion rates in steel through welding design. The researchers tested different welding groove designs (X, V, 1/2X, 1/2V) and preheating temperatures (400°C, 500°C, 600°C) on ferritic malleable iron samples. Testing found that X and V groove designs with 500°C and 600°C preheating had corrosion rates of 0.5-0.69% weight loss after 14 days, compared to 0.57-0.76% for 400°C preheating. Higher preheating reduced residual stresses which decreased corrosion. Residual stresses were 1.7 MPa for optimal X groove and 600°C
Router 1X3 – RTL Design and VerificationIJERD Editor
Routing is the process of moving a packet of data from source to destination and enables messages
to pass from one computer to another and eventually reach the target machine. A router is a networking device
that forwards data packets between computer networks. It is connected to two or more data lines from different
networks (as opposed to a network switch, which connects data lines from one single network). This paper,
mainly emphasizes upon the study of router device, it‟s top level architecture, and how various sub-modules of
router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top
module.
Active Power Exchange in Distributed Power-Flow Controller (DPFC) At Third Ha...IJERD Editor
This paper presents a component within the flexible ac-transmission system (FACTS) family, called
distributed power-flow controller (DPFC). The DPFC is derived from the unified power-flow controller (UPFC)
with an eliminated common dc link. The DPFC has the same control capabilities as the UPFC, which comprise
the adjustment of the line impedance, the transmission angle, and the bus voltage. The active power exchange
between the shunt and series converters, which is through the common dc link in the UPFC, is now through the
transmission lines at the third-harmonic frequency. DPFC multiple small-size single-phase converters which
reduces the cost of equipment, no voltage isolation between phases, increases redundancy and there by
reliability increases. The principle and analysis of the DPFC are presented in this paper and the corresponding
simulation results that are carried out on a scaled prototype are also shown.
Mitigation of Voltage Sag/Swell with Fuzzy Control Reduced Rating DVRIJERD Editor
Power quality has been an issue that is becoming increasingly pivotal in industrial electricity
consumers point of view in recent times. Modern industries employ Sensitive power electronic equipments,
control devices and non-linear loads as part of automated processes to increase energy efficiency and
productivity. Voltage disturbances are the most common power quality problem due to this the use of a large
numbers of sophisticated and sensitive electronic equipment in industrial systems is increased. This paper
discusses the design and simulation of dynamic voltage restorer for improvement of power quality and
reduce the harmonics distortion of sensitive loads. Power quality problem is occurring at non-standard
voltage, current and frequency. Electronic devices are very sensitive loads. In power system voltage sag,
swell, flicker and harmonics are some of the problem to the sensitive load. The compensation capability
of a DVR depends primarily on the maximum voltage injection ability and the amount of stored
energy available within the restorer. This device is connected in series with the distribution feeder at
medium voltage. A fuzzy logic control is used to produce the gate pulses for control circuit of DVR and the
circuit is simulated by using MATLAB/SIMULINK software.
Study on the Fused Deposition Modelling In Additive ManufacturingIJERD Editor
Additive manufacturing process, also popularly known as 3-D printing, is a process where a product
is created in a succession of layers. It is based on a novel materials incremental manufacturing philosophy.
Unlike conventional manufacturing processes where material is removed from a given work price to derive the
final shape of a product, 3-D printing develops the product from scratch thus obviating the necessity to cut away
materials. This prevents wastage of raw materials. Commonly used raw materials for the process are ABS
plastic, PLA and nylon. Recently the use of gold, bronze and wood has also been implemented. The complexity
factor of this process is 0% as in any object of any shape and size can be manufactured.
Spyware triggering system by particular string valueIJERD Editor
This computer programme can be used for good and bad purpose in hacking or in any general
purpose. We can say it is next step for hacking techniques such as keylogger and spyware. Once in this system if
user or hacker store particular string as a input after that software continually compare typing activity of user
with that stored string and if it is match then launch spyware programme.
A Blind Steganalysis on JPEG Gray Level Image Based on Statistical Features a...IJERD Editor
This paper presents a blind steganalysis technique to effectively attack the JPEG steganographic
schemes i.e. Jsteg, F5, Outguess and DWT Based. The proposed method exploits the correlations between
block-DCTcoefficients from intra-block and inter-block relation and the statistical moments of characteristic
functions of the test image is selected as features. The features are extracted from the BDCT JPEG 2-array.
Support Vector Machine with cross-validation is implemented for the classification.The proposed scheme gives
improved outcome in attacking.
Secure Image Transmission for Cloud Storage System Using Hybrid SchemeIJERD Editor
- Data over the cloud is transferred or transmitted between servers and users. Privacy of that
data is very important as it belongs to personal information. If data get hacked by the hacker, can be
used to defame a person’s social data. Sometimes delay are held during data transmission. i.e. Mobile
communication, bandwidth is low. Hence compression algorithms are proposed for fast and efficient
transmission, encryption is used for security purposes and blurring is used by providing additional
layers of security. These algorithms are hybridized for having a robust and efficient security and
transmission over cloud storage system.
Application of Buckley-Leverett Equation in Modeling the Radius of Invasion i...IJERD Editor
A thorough review of existing literature indicates that the Buckley-Leverett equation only analyzes
waterflood practices directly without any adjustments on real reservoir scenarios. By doing so, quite a number
of errors are introduced into these analyses. Also, for most waterflood scenarios, a radial investigation is more
appropriate than a simplified linear system. This study investigates the adoption of the Buckley-Leverett
equation to estimate the radius invasion of the displacing fluid during waterflooding. The model is also adopted
for a Microbial flood and a comparative analysis is conducted for both waterflooding and microbial flooding.
Results shown from the analysis doesn’t only records a success in determining the radial distance of the leading
edge of water during the flooding process, but also gives a clearer understanding of the applicability of
microbes to enhance oil production through in-situ production of bio-products like bio surfactans, biogenic
gases, bio acids etc.
Gesture Gaming on the World Wide Web Using an Ordinary Web CameraIJERD Editor
- Gesture gaming is a method by which users having a laptop/pc/x-box play games using natural or
bodily gestures. This paper presents a way of playing free flash games on the internet using an ordinary webcam
with the help of open source technologies. Emphasis in human activity recognition is given on the pose
estimation and the consistency in the pose of the player. These are estimated with the help of an ordinary web
camera having different resolutions from VGA to 20mps. Our work involved giving a 10 second documentary to
the user on how to play a particular game using gestures and what are the various kinds of gestures that can be
performed in front of the system. The initial inputs of the RGB values for the gesture component is obtained by
instructing the user to place his component in a red box in about 10 seconds after the short documentary before
the game is finished. Later the system opens the concerned game on the internet on popular flash game sites like
miniclip, games arcade, GameStop etc and loads the game clicking at various places and brings the state to a
place where the user is to perform only gestures to start playing the game. At any point of time the user can call
off the game by hitting the esc key and the program will release all of the controls and return to the desktop. It
was noted that the results obtained using an ordinary webcam matched that of the Kinect and the users could
relive the gaming experience of the free flash games on the net. Therefore effective in game advertising could
also be achieved thus resulting in a disruptive growth to the advertising firms.
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LLC resonant frequency converter is basically a combo of series as well as parallel resonant ckt. For
LCC resonant converter it is associated with a disadvantage that, though it has two resonant frequencies, the
lower resonant frequency is in ZCS region [5]. For this application, we are not able to design the converter
working at this resonant frequency. LLC resonant converter existed for a very long time but because of
unknown characteristic of this converter it was used as a series resonant converter with basically a passive
(resistive) load. . Here, it was designed to operate in switching frequency higher than resonant frequency of the
series resonant tank of Lr and Cr converter acts very similar to Series Resonant Converter. The benefit of LLC
resonant converter is narrow switching frequency range with light load[6] . Basically, the control ckt plays a
very imp. role and hence 555 Timer used here provides a perfect square wave as the control ckt provides no
slew rate which makes the square wave really strong and impenetrable. The dead band circuit provides the
exclusive dead band in micro seconds so as to avoid the simultaneous firing of two pairs of IGBT’s where one
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each and every ckt used because it acts as a driver and an isolation to each of the IGBT is provided with one
exclusive transformer supply[3]. The IGBT’s are fired using the appropriate signal using the previous boards
and hence at last a high frequency rectifier ckt with a filtering capacitor is used to get an exact dc
waveform .The basic goal of this particular analysis is to observe the wave forms and characteristics of
converters with differently positioned passive elements in the form of tank circuits. The supported simulation
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data sets of human genome „Y‟. The proposed system uses string matching with sliding window approach to
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Importance of Measurements in Smart GridIJERD Editor
- The need to get reliable supply, independence from fossil fuels, and capability to provide clean
energy at a fixed and lower cost, the existing power grid structure is transforming into Smart Grid. The
development of a smart energy distribution grid is a current goal of many nations. A Smart Grid should have
new capabilities such as self-healing, high reliability, energy management, and real-time pricing. This new era
of smart future grid will lead to major changes in existing technologies at generation, transmission and
distribution levels. The incorporation of renewable energy resources and distribution generators in the existing
grid will increase the complexity, optimization problems and instability of the system. This will lead to a
paradigm shift in the instrumentation and control requirements for Smart Grids for high quality, stable and
reliable electricity supply of power. The monitoring of the grid system state and stability relies on the
availability of reliable measurement of data. In this paper the measurement areas that highlight new
measurement challenges, development of the Smart Meters and the critical parameters of electric energy to be
monitored for improving the reliability of power systems has been discussed.
Study of Macro level Properties of SCC using GGBS and Lime stone powderIJERD Editor
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International Journal of Engineering Research and Development (IJERD)
1. International Journal of Engineering Research and Development
e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com
Volume 8, Issue 4 (August 2013), PP.53-64
53
Advances in Noise Removal and Image Filtering using
Fuzzy
Himadri Nath Moulick1
, Arun Kanti Manna2
, Joyjit Patra3
1,3
(Asst.Prof, C.S.E. dept.,Aryabhatta Institute Of Engineering And Management, Durgapur, W.B)
2
Asst.Prof, C.S.E. dept.,Modern Institute Of Engineering And Technology,Bandel,Hooghly W.B
Abstract:- In this paper we give an overview of the advances made in image and video filtering using fuzzy
logic, at our Fuzziness and Uncertainty Modeling Laboratory. The fact that fuzzy techniques have found an
interesting application field in image and video filtering is not a surprise: detecting whether a pixel is corrupted
by noise and assessing the degree to which such a pixel is corrupted are intrinsically fuzzy processes,that come
along with uncertainty (is the pixel noisy or not?)and imprecision (how noisy is it?).This paper proposes an
intelligent Furry lmqe Fdter (FIF)to remove impulse noise.The filter including two processes, the ImeUigenr
Fuizy Number Deciding (mVD) pmcp~s and fuzzy inJkencc pmcess, to filter impulse noise from heavily
corrupted images efiiciently.mVD can automatically decide the number of furzy number based on image
features to overcome the drawbacks of Adoptive Weighted Furzy Mean (AWFM) filter that must be defined by
domain expert Moreover,the fuzzy inference process refers the howledge base produced by IFND and fuzzy rule
base that can improve the wealole ss of conventional filters in heavily corrupted condition.The intelligent FIF
achieves better performance than the other filters based on the criteria of Mean Absolute Error (MAD,and Mean
Square Error (MSE).By the experiments,FIF still keeps the high performance to filtering impulse noise from
calor image.
Keywords:- Furzy number,AWFM filter,image processing, edge detection, impulse noise.
I. INTRODUCTION
Images and image sequences are among the most important information carriers in today‟s society, and
have applications in a wide variety of fields (industrial, commercial,entertainment,medical,military,....).The
power of images is that they can provide a lot of information in the blink of an eye.Due to bad
acquisition,transmission or recording,the images are however often corrupted by noise.A preprocessing module
to denoise the images then becomes necessary.For example,satellite images have to be denoised before ground
structures can be detected,and surveillance images have to be denoised before face recognition algorithms can
be applied.Inspired by the potential that fuzzy set theory has to offer in the field of image processing,our
Laboratory works –
already for a decade–on the topic of image and video noise filtering.As noise detection is uncertain and
noise removal is imprecise, fuzzy set theory and fuzzy logic turn out to be very valuable tools to develop new
algorithms for image and video denoising.It has also been shown that so-called“fuzzy filters”outperform their
classical counterparts,both in terms of numerical(e.g.,using Mean Square Error or Peak-Signalto-Noise-
Ratio)and visual evaluation.We briefly review the basics of fuzzy set theory and fuzzy logic in Section
2.Sections 3 and 4 are devoted to a series of fuzzy filters,respectively for still images (grayscale and color)and
image sequences(grayscale and color).In both cases we have developed filters for two very common noise types:
impulse noise (where a fraction of the pixel values is replaced by either fixed noise values or random noise
values) and gaussian noise (addititive noise);see Table I for an overview.These filters have been subject to
comparative studies with other state-of-the-art filters, in order to demonstrate their value.Our goal here is not to
give detailed technical explanations about the developed filters (16 in total),but to give the reader an overview
of our work on this topic during the past decade (including comparative studies), and to show that fuzzy set
theory and fuzzy logic are useful tools in image processing.
A Summary of the Different Fuzzy Filters for Noise Reduction That Were Developed In Our Laboratory.
2. Advances in Noise Removal and Image Filtering using Fuzzy
54
However,the capability of conventional filters based on pure numerical computation 6 broken down
rapidly when they are put in heavily noisy environment.There are many different methods of image processing
we can get rid of noise.Median filter is the most used method [I],but it will not work efficiently when the noise
rate is above 0.5. Yang and Tob [Z] used heuristic rules to improve the performance of traditional multilevel
median filter.Russo and Ramponi [3] applied heuristic knowledge to build fuzzy rule based operators for
smoothing, sharpening and edge detection.They can perform smoothing efficiently and preserving edges well.
Choi and Krishnapuram [4] used a powerful robust approach to image enhancement based on fuzzy logic
approach,which can remove impulse noise,smoothing out nowimpulse noise,and preserve edge
well.Besides,there are still many methods for removing impulse noise [S-71.The common drawback of these
methods is that they are sensitive to impulse noise when the noise rate becomes high.Weighted Fuzzy Mean
(WFM) filter [8] has a better ability of image processing for high impulse noise.Especially when the noise is
above 50% the traditional method for the image processing have no effect but WFM filter can still maintain a
steady result. Adaptive Weighted Fuzzy Mem (A WFM) [SI filter can improve the WFM filter‟s incapability in
a less noisy environment but still retain its capability of processing in the heavily noisy environment.The only
defect of A WFMis that the number of fizy numbes are being decided by a domain expert and not generated
automatically by the system, thus this paper proposes a method to automatically construct the fizy numbers for
the intelligent IW.
II. DECIDING PROCESS FOR RFMOVING IMPULSE NOISE
The characteristics of images are very suited to be represented by fuzzy numbers (81).Due to the
extreme difference in the characteristics of the images,the simple adoption of fixed furzy numben cannot
completely contain the characteristics of the full image.This section propose an Intelligent Fuzzy Number
Deciding (IFND) process which can automatically decide the number of firzzy rumbas according to the
histogram of the image. Now we define the fuzzy numberas follows: [Definition 11].The fuzzy sers used in the
knowledge base of intelligent FIF are of the L R type fuzzy number [IO] formulated by the following equation:
...........(1)
Where G(y)=R(y)=max(O,l-y),andAx)on be represented23 atriplet [m,a,P] Figure 1 shows the filtering
process of intelligent ITF.
The IFND process refers the input image features to produce the respective fuzzy numben into the
knowledge base.The fuzzy inference process including fuzzy inference d e s and Middle Decision Process
(MDP) uses the fuzzy rule base and knowledge base to perform the middle filtering.The Final Decision Process
(FDP) will decide the final output of intelligent FIF.Figure 2 illustrates the process of generation
offuzrynumbers for A W m and ITF.
Fig.2(a)shows the histogram of an image. Fig.2(b)is the generation process of fizzy numbers for AWEMwhose
number is fixed at 5.Fig2 (c) is the generation process of fuzzy numbers for intelligent FIF and its number of
fuzzy numbers is not fixed.
The algorithm for IFND process of intelligent FIF is as follows:
3. Advances in Noise Removal and Image Filtering using Fuzzy
55
A. The algorithm for lFNDpmcess of intelligent FIF
Input: Noisocormpted image X , histogram
Outpt:Parametersset[m,a,p]oXf ;
Method:
Mliancevalue p ;
Stepl: Get the histogram of X .
Stepl.1: Get the start point X, of the histogram.
Stepl.2: Get the end point X, of the histogram.
StepZ: Get the mode Xde and it‟s count X,.-.,.ofthe h i s t o w .
Step2.l: m, t XdF;
Step3: For XGRAYLEVEL + Xd,@ X,
Step3.1: order, + ~~XG””“L, , /Xd4”;<
Step3.2:order, c(XGRAYL€Y€~-X,)/(X-,- X-);
Step33: M t O
Step3.4: hirvrr +- oniprl -order2;
Step3.4.1: ifhisvm>pthen
Step3.4.1.1: b+XGRAYLEYEL;
Step3.4.1.2 M e M+l;
Step3.4.2: Else if (binrar2p & M=O)
Step3.4.2.1: XmOd+.X ,*-l, go to Stepl
Step3.4.3.1: Get the mode &ode_~ nLxt of the histogram between band b+M.
Step3.43.2 :; ,m+,&,2,
Step3.5: Get the graylevel of minimum count of histogram between Kade and
Step3.5.1:(r,t ml-(gaylevel of minimum count of histogram between
Stepl.4 bisvmt orderl -0rder2;
Stepl.4.1: If bisvm>pthen
Step4.4.l.l: b+ XGRAYLEm
Step4.4.1.2: M+ M+l;
Step4.4.2: Else if (bisvm5P & M4)
Step4.4.2.1: Xmde +Xm&+l, go to Step4.
Step4.43: Else if (bisvm2P & MZ 0)
Stepl.4.3.1: Get the mode &ale.aert of the histogram between b and b+M.
Step4.4.2.2 mi
Step4.5: Get the graylevel of mirimum coutt of histogram between and
Step4.5.1: a, t mi -(graylevel of minimum count of histogram between & aleand&a-mat).
Step4.5.2: fl,(graylevel of minimum count of histogram between Ynd and % dht1m I
st@.5.3:xm*&xdem&&&.hat.
Step5: End
Figure 3 shows the graph representation of IFND process for intelligent FIF.By deciding the distance of orderl
and d e r 2 , IFND can intelligent construct the fuzzy numbers for represenhng the image features.
4. Advances in Noise Removal and Image Filtering using Fuzzy
56
III. FUZZY INFERENCE PROCESS FOR IMPULSE NOISE REMOVAL
Now we define the notations for fuzzy inference process. Let the corrupted image be denoted 23
x=[ .r( i , ,>, i=r~o n , j = i t om],the middle result of fuzzy inference process be denotedas Y = [ y ( i . j ) , i = i t
o n , j = l tom].and the final result of intelligent fuzzy image filter be Z = [ z ( i , j ),i = i r o n , j = 1 r o m ].The
fuzzy inference process of FIF is realized by the Sugenutyped inference approach[ I I ].The number of fuzzy
rules is according to the result of IFND process, that is, it is various for different image.For example,if the
number of fuzzy numbers produced by IFND is three,namely Dark (DO, Median (MD) and Bright (ER), then
the fuzzy infeence mules are shown as follows.
Rule 1: f x ( i - I , j-I) is OK, x(C1. j) is OK, xfi-I,j+l) is OK, x(i, j l ) is OK, xfi, j) is OK, x(i,j+l) is OK, x(i+l. j-
I) is DK, xfi+l. j) is OK.x(i+l, j+l) is DK
then
Rule2:if x( i4,jl)is MD,x(i-1,J) is MD,x(i-1,j+l)is MD,x(i,j-l)isMD,<i,j)is MD,x(i,fil)is MD,x(i+l, j-1)is MD,
x(i+l, j )is MD,x( i + l ,p l ) is MD then
Rule 3: ifx(i-1, j-1) is BR, x(i-I, j ) is BR, x(i-I,p l ) i s BR,x(i j-I) is ER, x(i j ) isER,x(i,pl) is ER,x(i+l,j-I) is ER,
x(i+l, j) is BR, rjitl, j + l )is ER then
The MDP is implemented by a weighted average approach for the three intermediate fupy inference results,that
is
.......2
where each weight wr is 1 if the anom of associated intermediate inference result y,(i, j )and the fizzy estimaior
result [SI is minimum;otherwise it is zero.Let x(i, 1) -A< 1) = S(i, j ),then we define the fizzy detecton for
evaluating the amplitudes of positive impulse noise and negative impulse noise as follows.[Definirion 21 The
fuzzy derecton FLIP,,(.) and FD,,(.) [7] are the mechanisms to detect the amplitudes of positive impulse noise b
o s andnegative impulse noise i n e g of the whole smeared image,respectively.If
Where and I-D-neg are the fuzzy intervals for detecting positive and negative impulse noise respectively,and
X=[x(i,j)l,+,is the received image. Otherwise, s-pos=o 2nd <-neg=O A fizzy signal space [7] is a signal space
5. Advances in Noise Removal and Image Filtering using Fuzzy
57
whose partitions are decided by fuzzy intervals.The partitions include fuzzy uncorrupted subspace,fuzzy
positive subspace,fuzzy negarive subspace and f w z y undecided subspace by the fuzzy uncompted interval,
fuzzy posirive interval,fizzy negative interval, and the fuzzy undecided interval respectively.Then the FDP
decides the final filtering result z(i, j )according to the following fuzzy i n k m e rules:
Rule FDPI: Ifthe distance ofxfi, j) and yfi, j) ir located in fizy uncorrupted subspace, then thefinal ourputzfi, j)
=x(i, j).
Rule FDPZ: If the distance ofx(i, j) and yfi, J] is locared in fuzzy undecided subspace then thefindouptzfi, J] =yo,
j).
RuleFDP3: If the distance ofx(i, j) and yo, j) is located in fwzy positive subspacr; then the final mrput z(i.1) =
x(i, j ) - 5p...
Rule FDP4: If the distance ofx(i. j) and yfi, j) is located in fuzzy negative subspace; then the finaloutputzlij) =
x(i, j
IV. RESULTS
There are many different methods for removing impulse noise from corrupted images [ 1-61],But when
the noise rate becomes high,the performance of these filters is broken down rapidly.In this paper,we implement
four different algorithms including A WFM jilter,Medimfilrer, Selection medianfilrer (SMF)[ 121 and FiF filter
to filter the heavily corrupted image.The experiments are performed on the image "Lenna" corrupted by additive
impulse noise.
Figure 4 sbJws the MAE curves for the four methods, where n is the filtering mask for SME Besides,
we also compare our method with the other filters including WFM, RCRS, CWM, WO$ and Sfark filters.
Figure 5 shows the curves of all compared filters for the MAE
criterion
Notice that the MAE curves of RCRS. CWM, WO$ and Stack filters are obtained by learning from a
512 by 512, 8 bitsipixel image of“Albert”, and then filtering a 512 by 512, 8 bitsipixel image of “Lenna” [5].
However, in our experiment, the “Lenna” image is sized 256 by 256 pixels with the same gray level resolution.
Since it is difficult to judge the performance of image removal processing algorithm based solely quantitative
analysis, we show some filtered results for subjective evaluation.
6. Advances in Noise Removal and Image Filtering using Fuzzy
58
Figure 6 shows the “Lenna” image corrupted by 90% additive impulse noise and the filtered results.
Figure 6(a) to Figure 6(f) show the noisefree image, heavily corrupted image, result ofAWFM, result of median
filter, result of SME and result of FIF, respectively.
Figure 7 shows the edge detection results of AWFM, median filter, SMF, and FIF, respectively. We also apply
the four filters to color image processing. Figure 8 shows the filtering results of color image “Lenna”.
Figure 8(a) to Figure 8(f) show the noise-free color image, heavily corrupted color image, result of AWFM,
result of median filter, result of SME and result of FIF, respectively.
7. Advances in Noise Removal and Image Filtering using Fuzzy
59
Figure 9 shows the edge detection results of AWFM, medim filter, SMF; and FIF, for color image “Lenna”
respectively. Finally, the comparisms of MAE and MSE for the gray level image “Lenna” and its color image
version are shown in Table 1 and Table 2, rerpectively.
V. FUZZY SET THEORY AND FUZZY LOGIC
A crisp set in a universe X is characterized by an X−{0, 1} mapping,where 1 indicates that an element
belongs to the set and 0 indicates it doesn‟t.A fuzzy set A in a universe X is characterized by an X − [0, 1]
mapping μA, called the membership function [1],where μA(x) indicates the degree to which the element x in X
belongs to the set A or satisfies the property expressed by the set A.In other words,fuzzy sets allow membership
degrees between 0 and 1 and thus a more gradual transition between “belonging to” and “not belonging to”.This
makes fuzzy sets very useful for the processing of human knowledge, where linguistic values (e.g. large,
small, . . . ) are used. For example,a difference in gray level is not necessarily small or not small, but can be
small to some degree.A possible embership function of the fuzzy set small is given in Figure 10.The extension
from crisp to fuzzy sets comes along with an extension of the underlying binary logical framework to fuzzy
logic.In fuzzy logic,expressions can be true or false to a
8. Advances in Noise Removal and Image Filtering using Fuzzy
60
Fig.10.A possible membership function of the fuzzy set small (the parameter K can be chosen by the
user,depending on the application.)
The closer an element is to 0, the higher its membership value is. certain degree, and consequently we
should be able to connect such expressions (with the logical NOT, AND, OR, . . .) using fuzzy logical operators
that extend their binary counterparts.This can be achieved by using fuzzy logical operators,such as negators
(NOT),conjunctors (AND) and disjunctors (OR). Formally [2],a negator N is a decreasing [0, 1] − [0, 1]
mapping that satisfies N(0) = 1 and N(1) = 0,a conjunctor C is an increasing [0, 1]×[0, 1]−[0, 1] mapping that
satisfies C(0, 0) = C(1, 0) = C(0, 1) = 0 and C(1, 1) = 1,and a disjunctor D is an increasing [0, 1]×[0, 1]−[0, 1]
mapping that satisfies D(1, 1) = D(1, 0) = D(0, 1) = 1 and D(0, 0) = 0.
The boundary conditions ensure that these fuzzy operators are real extensions of the binary NOT,AND and OR.
Popular examples are Ns(a) = 1 − a, CM(a, b) = min(a, b) and CP (a, b) = a · b, DM(a, b) = max(a, b) and DP (a,
b) = a + b − a · b,respectively,with a, b 2 [0, 1].Having fuzzy sets to model linguistic values and fuzzy logic to
reason with them, fuzzy rules can be used to model human reasoning and to derive new (imprecise) knowledge
from given (imprecise) knowledge.An example of a fuzzy rule is an expression of the form IF((p is P AND q is
Q) OR (r is NOT R)),THEN (s is S),with P,Q,R,S fuzzy sets (modeling linguistic values)and p, q, r, s elements
from the corresponding universes.The degree S(s) to which “s is S” (e.g., to which a pixel is considered noisy) is
given by the degree to which the antecedent of the rule (i.e., the IF-part) is true.This degree is given by
S(s)=D(C(P(p),Q(q)),N(R(r))),using a disjunctor D,a conjunctor C and a negator N.With the above tools we are
able to create a mathematical model for human reasoning with imprecise knowledge.
VI. FUZZY FILTERS FOR STILL IMAGES
The main advantage of fuzzy filters is that they allow us to work and to reason with linguistic
information, just as experts do (approximate reasoning);see the scheme in Figure 11.Our work on image
denoising started with the so-called GOA filter [3].The filter is designed for the removal of Gaussian noise in
grayscale images,and uses fuzzy rules to detect the degree to which the gradient in a certain direction is
small.The idea is that a small gradient is caused by noise,while a large gradient is caused by image structure;see
Table II for an example.Fuzzy rules are also applied to calculate the correction term that is used for the
denoising;the contribution of neighbouring pixels depends on their gradient values.The results of the GOA filter
were very good,and demonstrated the usefulness of fuzzy logic for the construction of noise reduction filters.In
order to confirm these good results we
carried out extensive comparative studies of existing classical and fuzzy filters,including the mean filter,the
adaptive Wiener filter [4],fuzzy median (FM) [5],the adaptive weighted fuzzy mean (AWFM1 and AWFM2) [6],
[7],the iterative fuzzy filter (IFC),the modified iterative fuzzy filter (MIFC),and the extended iterative fuzzy
filter (EIFC) [8].
Fig. 11. Fuzzy filters not only use numerical information to filter out the noise in images, but can also work with
linguistic information. Furthermore, fuzzy logic allows us to reason with this linguistic information and enables
us to better approximate human reasoning.
Table 3
A Fuzzy Rule That Models The Following Reasoning (See [3] For Details):If The Fuzzy Gradient Of A
Pixel(I,J)In The North-West (Nw) Direction Is Small And Its Actual Gradient (The Difference Between The
9. Advances in Noise Removal and Image Filtering using Fuzzy
61
Pixel And Its Neighbour In The Direction Nw) Has A Positive Value,Then The Correction Term C For That
Pixel Has A Positive Value. The Fuzzy Gradient Is Calculated Using Gradient Values Of The Pixel(I,J)And Its
Neighbours Perpendicular To The Considered Direction;It Is Used To Differentiate Between Gradient Values
Caused By Noise And Gradient Values Caused By An Edge In The Image.
A second filter for the reduction of gaussian noise from grayscale images was presented a few years
later [9]. This FuzzyShrink-filter can be seen as a fuzzy variant of an existing probabilistic shrinkage method,
and was developed in the wavelet domain. The filter outperformed fuzzy non-wavelet methods, such as the
histogram adaptive fuzzy filter (HAF) [10], the EIFC filter, the smoothing fuzzy control based filter (SFCF) [11],
the decreasing weight fuzzy filter with moving average centre (DWMAV) [12], the adaptive fuzzy switching
filter (AFSF) [13], the fuzzy similarity filter (FSB) [14], and the AWFM. It also was comparable with other
recent but more complex wavelet methods, including the bivariate wavelet shrinkage method [15], the feature-
based wavelet shrinkage method from [16] and the probabilistic shrinkage method [17].After the succesfull
GOA filter for gaussian noise, we developed the Fuzzy Impulse noise Detection and Reduction Method
(FIDRM [18]) for the removal of fixed impulse noise in grayscale images. The filter followed a similar
approach.
Fig. 12. Noise removal from the Mandrill image:
Top = part of the original image,middle=image contaminated with 50% impulse noise (salt & pepper
noise), bottom = denoised result with the FIDRM filter as the GOA filter, as it used gradient values to detect and
remove the noise. The visual results are quite spectacular, as shown in Figure 12. Again, extensive experiments
confirmed the state-of-the-art results of the filter. The filter could easily be extended to color images by
applying the filter on each of the color bands separately. The results for color images were relatively good
[19],but the disadvantage of this approach is that correlations between color bands are neglected and small color
10. Advances in Noise Removal and Image Filtering using Fuzzy
62
artefacts are introduced.This inspired us to construct other filters, specifically to remove impulse noise from
color images, and led to the FIDRMC and HFMRC filters.The FIDRMC filter consists of two separated
steps:the detection phase and the filtering phase.The detection phase is applied separately to each color
component,where fuzzy rules are used to determine whether a pixel pigment is corrupted with impulse noise or
not.After the detection phase the filter only focuses on those pixel pigments which have a non-zero membership
degree in the fuzzy set “impulse noise”.In the filtering phase we also take into account the color information of a
certain neighbourhood around a given central pixel [20].The HFMRC filter follows a different approach and
uses the histograms of the color component differences to detect and filter the fixed impulse noise [21].The
HFMRC filter was later upgraded to the more complex HFC filter [22] that could also tackle randomly valued
impulse noise in color images. Previously,our FRINR filter already achieved the goal of removing randomly
valued impulse noise in grayscale images [23].The detection phase of the FRINR filter consists of two units that
are both used to define corrupted impulse noise pixels.The first unit investigates the neighbourhood around a
pixel to conclude whether the pixel can be considered as impulse noise or not,while the second unit uses fuzzy
gradient values to determine the degree to which a pixel can be considered as impulse noise and the degree to
which a pixel can be considered as noise free. For the comparative studies, several other filters were considered.
A first group of filters are grayscale filters that were extended to application on color images (see previous
comparative studies),and a second group of filters are vector filters that were designed specifically for color
images.It concerns the fuzzy vector rank filter (FVRF) [24],the fuzzy credibility color filter (FCCF) [25] and the
adaptive vector median filter (AVMF) [26]. Regarding the removal of gaussian noise from color images, we
developed the FCG filter [27].In contrast to most other methods, the first subfilter of the FCG filter
distinguishes between local variations due to noise and local variations due to image structures (such as edges)
by using the color component distances instead of component differences.The second subfilter is used as a
complementary filter which especially preserves differences between the color components.Filters in the
comparative study include the hidden Markov tree method (HMT) [28], the 3D-DFT method [29], the Bayesian
least squares - Gaussian scale mixture filter (BLS-GSM) [30], the bivariate shrinkage method,the chromatic
filter proposed in [31],and the total least square filter (TLS) [32].
VII. CONCLUSION
The power of fuzzy filters is that they can model human (approximate) reasoning, using linguistic
variables in the reasoning process. Fuzzy set theory and fuzzy logic provide the tools, and comparative studies
demonstrate that fuzzy filters can outperform classical approaches. We certainly do not want to claim that fuzzy
set theory is “the way to go”, but where applicable it can lead to an improvement of image processing results.
In this paper, we have proposed an intelligent fuzzy image filter for additive impulse noise removal. The
intelligent FIF contains two processes, IJWD process and fuzzy inference process, to perform the efficient
recovery task. IFND process can generate the fizzy numbers of the specified image automatically and store them
into the knowledge base. Then the fwzy infemence process refers the knowledge base and fuzzy rule base to
execute the fuzzy inference. Furthennore, the FDPwill decide the final output by the decision rules.For image
detection, we adopt the Sobel operator to work with the filters. n e experimental results show that FIF achieve
the most efficient for removing heavily corrupted additive impulse noise. In the future, we will refine this
method to make it can deal with various noise models such as Gaussian impulse noise. Besides, the edge
detection algorithm for noise image will also be developed.
ACKNOWLEDGMENT
The Authors Are Thankful To Mr.Saikat Maity & Dr.Chandan Konar For The Support To Develop
This Document.
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