Image de-noising is the technique to reduce noises from corrupted images. The aim of the image denoising is to improve the contrast of the image or perception of information in images for human viewers or to provide better output for other automated image processing techniques. This paper presents a new approach for color image de-noising with Fuzzy Filtering techniques using centroid method for defuzzification. It preserves any type of edges (including tiny edges) in any direction. The experimental result shows the effectiveness of the proposed method.
IRJET - Change Detection in Satellite Images using Convolutional Neural N...IRJET Journal
The document describes a method for detecting changes in satellite images using convolutional neural networks. It discusses how existing methods have limitations in terms of accuracy and speed. The proposed method uses preprocessing techniques like median filtering and non-local means filtering. It then applies convolutional neural networks to extracted compressed image features and classify detected changes. The method forms a difference image without explicitly training on change images, making it unsupervised. Testing achieved 91.63% accuracy in change detection, showing the effectiveness of the proposed convolutional neural network approach.
This document proposes a new dual threshold median filter called Dual Threshold Median Filter (DTMF) for removing random valued impulse noise from digital images while preserving edges. The algorithm has two main stages: noise detection and noise removal. In the detection stage, the maximum and minimum pixel values in a 3x3 window are used to classify the central pixel as noisy or noise-free. Noisy pixels are then replaced in the removal stage using median filtering. The proposed filter is tested on standard images like Lena and Mandrill corrupted with 3-99% random valued impulse noise. Results show it achieves better peak signal-to-noise ratios and lower mean squared errors than previous methods, especially at high noise densities, indicating it effectively
Image Noise Removal by Dual Threshold Median Filter for RVINIOSR Journals
The document proposes a dual threshold median filter (DTMF) for removing random valued impulse noise from digital images while preserving edges. It first detects impulse noise pixels based on maximum and minimum pixel values in a 3x3 window. It then removes the detected noise using median filtering. In high noise densities, it can be difficult to identify noisy pixels or image edges. The proposed filter addresses this by analyzing noisy and noise-free pixels to provide better visual quality in the de-noised image compared to previous methods, as shown by its higher peak signal-to-noise ratio and lower mean squared error on test images with different noise densities.
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
IRJET- A Review on Various Restoration Techniques in Digital Image ProcessingIRJET Journal
The document reviews various image restoration techniques used for removing noise and blurring from digital images. It discusses techniques like median filtering, Wiener filtering, and Lucy Richardson algorithms. It provides an overview of each technique, including their advantages and limitations. The document also reviews several research papers that propose modifications to existing techniques or new methods for tasks like salt-and-pepper noise removal. The reviewed papers found that their proposed methods improved restoration quality over other techniques, achieving higher PSNR values and producing images that looked visually sharper and more distinct.
Visual Quality for both Images and Display of Systems by Visual Enhancement u...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...IJAAS Team
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
IRJET - Change Detection in Satellite Images using Convolutional Neural N...IRJET Journal
The document describes a method for detecting changes in satellite images using convolutional neural networks. It discusses how existing methods have limitations in terms of accuracy and speed. The proposed method uses preprocessing techniques like median filtering and non-local means filtering. It then applies convolutional neural networks to extracted compressed image features and classify detected changes. The method forms a difference image without explicitly training on change images, making it unsupervised. Testing achieved 91.63% accuracy in change detection, showing the effectiveness of the proposed convolutional neural network approach.
This document proposes a new dual threshold median filter called Dual Threshold Median Filter (DTMF) for removing random valued impulse noise from digital images while preserving edges. The algorithm has two main stages: noise detection and noise removal. In the detection stage, the maximum and minimum pixel values in a 3x3 window are used to classify the central pixel as noisy or noise-free. Noisy pixels are then replaced in the removal stage using median filtering. The proposed filter is tested on standard images like Lena and Mandrill corrupted with 3-99% random valued impulse noise. Results show it achieves better peak signal-to-noise ratios and lower mean squared errors than previous methods, especially at high noise densities, indicating it effectively
Image Noise Removal by Dual Threshold Median Filter for RVINIOSR Journals
The document proposes a dual threshold median filter (DTMF) for removing random valued impulse noise from digital images while preserving edges. It first detects impulse noise pixels based on maximum and minimum pixel values in a 3x3 window. It then removes the detected noise using median filtering. In high noise densities, it can be difficult to identify noisy pixels or image edges. The proposed filter addresses this by analyzing noisy and noise-free pixels to provide better visual quality in the de-noised image compared to previous methods, as shown by its higher peak signal-to-noise ratio and lower mean squared error on test images with different noise densities.
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
IRJET- A Review on Various Restoration Techniques in Digital Image ProcessingIRJET Journal
The document reviews various image restoration techniques used for removing noise and blurring from digital images. It discusses techniques like median filtering, Wiener filtering, and Lucy Richardson algorithms. It provides an overview of each technique, including their advantages and limitations. The document also reviews several research papers that propose modifications to existing techniques or new methods for tasks like salt-and-pepper noise removal. The reviewed papers found that their proposed methods improved restoration quality over other techniques, achieving higher PSNR values and producing images that looked visually sharper and more distinct.
Visual Quality for both Images and Display of Systems by Visual Enhancement u...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...IJAAS Team
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
The Performance Analysis of Median Filter for Suppressing Impulse Noise from ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document analyzes the performance of the median filter for suppressing impulse noise from images. It applies median filtering to low, medium, and high detail images corrupted with varying densities of salt-and-pepper impulse noise from 1% to 60%. The median filter's performance is evaluated based on its edge-preserving capabilities through edge detection, subjective analysis via visual quality, and objective analysis using mean squared error, peak signal-to-noise ratio, and mean absolute error. The results show that median filtering effectively suppresses low-density impulse noise while preserving edges, though it can blur edges slightly due to uniform filtering and modify some uncorrupted pixels. Overall, the median filter performs better than linear filters for impulse noise removal from
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.
This document discusses using smoothing filters based on rough set theory for medical image enhancement. It introduces common smoothing filters like mean, median, mode, and triangular filters. These filters can reduce noise and enhance edges in medical images. The document proposes a parallel rough set based model that implements multiple smoothing filters at once to obtain independent results and generate an enhanced mean image for improved medical image quality and complex image processing.
THE EFFECT OF IMPLEMENTING OF NONLINEAR FILTERS FOR ENHANCING MEDICAL IMAGES ...ijcsit
Although this huge development in medical imaging tools, we find that there are some human mistakes in the process of filming medical images, where some errors result in distortions in the image and change some medical image properties which affect the disease diagnosis correctly.Medical images are one of the fundamental images, because they are used in the most sensitive field which is a medical field. The
objective of the study is to identify the effect of implement non-linear filters in enhancing medical images,using the strongest and most popular program MATLAB, and because of its advantages in image processing. After implementation the researcher concluded that we will get the best result for medical image enhancement by using median filter which is one of the non-linear filters,non-linear filters implemented using Matlab functions
This document provides an introduction to fundamentals of image processing. It defines key concepts such as digital images, image sampling, and common image processing tools. Digital images are represented as arrays of pixels with integer brightness values. Common image processing tools introduced include convolution, Fourier transforms, and different types of image operations and neighborhoods that can be used. The document also discusses video standards and parameters for digitized video images.
Translation Invariance (TI) based Novel Approach for better De-noising of Dig...IRJET Journal
1. The document discusses a novel Translation Invariance (TI) approach for improving the performance of various digital image processing filters for image denoising.
2. It describes applying filters like convolution, wiener, gaussian etc. both without TI (directly on noisy image) and with TI (by shifting the image and averaging results) to denoise images.
3. The results found that using the TI approach, where the filters are applied after shifting the image and averaging the outputs, produced better performance and noise removal compared to directly applying the filters without translation invariance. This was also verified using edge detection tests.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a new algorithm to reduce blocking artifacts in compressed images using a combination of the SAWS technique, Fuzzy Impulse Artifact Detection and Reduction Method (FIDRM), and Noise Adaptive Fuzzy Switching Median Filter (NAFSM). FIDRM uses fuzzy rules to detect noisy pixels, while NAFSM uses a median filter to correct pixels based on local information. Experimental results on test images show the proposed approach achieves better PSNR than other deblocking methods.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJCER, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, research and review articles, IJCER Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathematics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer review journal, indexed journal, research and review articles, engineering journal, www.ijceronline.com, research journals,
yahoo journals, bing journals, International Journal of Computational Engineering Research, Google journals, hard copy of Certificate,
journal of engineering, online Submission
Color image compression based on spatial and magnitude signal decomposition IJECEIAES
In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.
Performance of Various Order Statistics Filters in Impulse and Mixed Noise Re...sipij
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise
removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
Pedestrian detection under weather conditions using conditional generative ad...IAESIJAI
Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a study on medial axis transformation (MAT) based skeletonization of image patterns using image processing techniques. It discusses how the MAT of an image can be extracted by first computing the Euclidean distance transform of the binary image. Local maxima in the distance transform image correspond to the MAT. Several performance evaluation metrics for analyzing skeletonized images are also introduced, such as connectivity number, thinness measurement and sensitivity. The technique is demonstrated on sample images and results show it can effectively extract the skeleton with good computational speed.
n every image processing algorithm quality of ima
ge plays a very vital role because the output of th
e
algorithm depends on the quality of input image. He
nce, several techniques are used for image quality
enhancement and image restoration. Some of them are
common techniques applied to all the images
without having prior knowledge of noise and are cal
led image enhancement algorithms. Some of the image
processing algorithms use the prior knowledge of th
e type of noise present in the image and are referr
ed to
as image restoration techniques. Image restoration
techniques are also referred to as image de-noising
techniques. In such cases, identified inverse degra
dation functions are used to restore images. In thi
s
survey, we review several impulse noise removal tec
hniques reported in the literature and identify eff
icient
implementations. We analyse and compare the perform
ance of different reported impulse noise reduction
techniques with Restored Mean Absolute Error (RMAE)
under different noise conditions. Also, we identif
y
the most efficient impulse noise removing filters.
Marking the maximum and minimum performance of
filters helps in designing and comparing the new fi
lters which give better results than the existing f
ilters.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
The document describes a novel algorithm for despeckling synthetic aperture radar (SAR) images using particle swarm optimization (PSO) in the curvelet domain. The algorithm first identifies homogeneous regions in the speckled image using variance calculations. It then uses PSO to optimize the thresholding of curvelet coefficients, with the objective of minimizing the average power spectral value. This provides an optimized threshold to apply curvelet-based despeckling. The proposed method is tested on standard images and shown to outperform conventional filters like median and Lee filters in reducing speckle noise.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
Image denoising using new adaptive based median filtersipij
Noise is a major issue while transferring images through all kinds of electronic communication. One of the
most common noise in electronic communication is an impulse noise which is caused by unstable voltage.
In this paper, the comparison of known image denoising techniques is discussed and a new technique using
the decision based approach has been used for the removal of impulse noise. All these methods can
primarily preserve image details while suppressing impulsive noise. The principle of these techniques is at
first introduced and then analysed with various simulation results using MATLAB. Most of the previously
known techniques are applicable for the denoising of images corrupted with less noise density. Here a new
decision based technique has been presented which shows better performances than those already being
used. The comparisons are made based on visual appreciation and further quantitatively by Mean Square
error (MSE) and Peak Signal to Noise Ratio (PSNR) of different filtered images..
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The Performance Analysis of Median Filter for Suppressing Impulse Noise from ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document analyzes the performance of the median filter for suppressing impulse noise from images. It applies median filtering to low, medium, and high detail images corrupted with varying densities of salt-and-pepper impulse noise from 1% to 60%. The median filter's performance is evaluated based on its edge-preserving capabilities through edge detection, subjective analysis via visual quality, and objective analysis using mean squared error, peak signal-to-noise ratio, and mean absolute error. The results show that median filtering effectively suppresses low-density impulse noise while preserving edges, though it can blur edges slightly due to uniform filtering and modify some uncorrupted pixels. Overall, the median filter performs better than linear filters for impulse noise removal from
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.
This document discusses using smoothing filters based on rough set theory for medical image enhancement. It introduces common smoothing filters like mean, median, mode, and triangular filters. These filters can reduce noise and enhance edges in medical images. The document proposes a parallel rough set based model that implements multiple smoothing filters at once to obtain independent results and generate an enhanced mean image for improved medical image quality and complex image processing.
THE EFFECT OF IMPLEMENTING OF NONLINEAR FILTERS FOR ENHANCING MEDICAL IMAGES ...ijcsit
Although this huge development in medical imaging tools, we find that there are some human mistakes in the process of filming medical images, where some errors result in distortions in the image and change some medical image properties which affect the disease diagnosis correctly.Medical images are one of the fundamental images, because they are used in the most sensitive field which is a medical field. The
objective of the study is to identify the effect of implement non-linear filters in enhancing medical images,using the strongest and most popular program MATLAB, and because of its advantages in image processing. After implementation the researcher concluded that we will get the best result for medical image enhancement by using median filter which is one of the non-linear filters,non-linear filters implemented using Matlab functions
This document provides an introduction to fundamentals of image processing. It defines key concepts such as digital images, image sampling, and common image processing tools. Digital images are represented as arrays of pixels with integer brightness values. Common image processing tools introduced include convolution, Fourier transforms, and different types of image operations and neighborhoods that can be used. The document also discusses video standards and parameters for digitized video images.
Translation Invariance (TI) based Novel Approach for better De-noising of Dig...IRJET Journal
1. The document discusses a novel Translation Invariance (TI) approach for improving the performance of various digital image processing filters for image denoising.
2. It describes applying filters like convolution, wiener, gaussian etc. both without TI (directly on noisy image) and with TI (by shifting the image and averaging results) to denoise images.
3. The results found that using the TI approach, where the filters are applied after shifting the image and averaging the outputs, produced better performance and noise removal compared to directly applying the filters without translation invariance. This was also verified using edge detection tests.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a new algorithm to reduce blocking artifacts in compressed images using a combination of the SAWS technique, Fuzzy Impulse Artifact Detection and Reduction Method (FIDRM), and Noise Adaptive Fuzzy Switching Median Filter (NAFSM). FIDRM uses fuzzy rules to detect noisy pixels, while NAFSM uses a median filter to correct pixels based on local information. Experimental results on test images show the proposed approach achieves better PSNR than other deblocking methods.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJCER, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, research and review articles, IJCER Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathematics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer review journal, indexed journal, research and review articles, engineering journal, www.ijceronline.com, research journals,
yahoo journals, bing journals, International Journal of Computational Engineering Research, Google journals, hard copy of Certificate,
journal of engineering, online Submission
Color image compression based on spatial and magnitude signal decomposition IJECEIAES
In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.
Performance of Various Order Statistics Filters in Impulse and Mixed Noise Re...sipij
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise
removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
Pedestrian detection under weather conditions using conditional generative ad...IAESIJAI
Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a study on medial axis transformation (MAT) based skeletonization of image patterns using image processing techniques. It discusses how the MAT of an image can be extracted by first computing the Euclidean distance transform of the binary image. Local maxima in the distance transform image correspond to the MAT. Several performance evaluation metrics for analyzing skeletonized images are also introduced, such as connectivity number, thinness measurement and sensitivity. The technique is demonstrated on sample images and results show it can effectively extract the skeleton with good computational speed.
n every image processing algorithm quality of ima
ge plays a very vital role because the output of th
e
algorithm depends on the quality of input image. He
nce, several techniques are used for image quality
enhancement and image restoration. Some of them are
common techniques applied to all the images
without having prior knowledge of noise and are cal
led image enhancement algorithms. Some of the image
processing algorithms use the prior knowledge of th
e type of noise present in the image and are referr
ed to
as image restoration techniques. Image restoration
techniques are also referred to as image de-noising
techniques. In such cases, identified inverse degra
dation functions are used to restore images. In thi
s
survey, we review several impulse noise removal tec
hniques reported in the literature and identify eff
icient
implementations. We analyse and compare the perform
ance of different reported impulse noise reduction
techniques with Restored Mean Absolute Error (RMAE)
under different noise conditions. Also, we identif
y
the most efficient impulse noise removing filters.
Marking the maximum and minimum performance of
filters helps in designing and comparing the new fi
lters which give better results than the existing f
ilters.
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An Unique Edge Preserving Noise Filtering Technique for Impulse Noise Removal
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
DOI : 10.5121/sipij.2011.2304 33
AN UNIQUE EDGE PRESERVING NOISE
FILTERING TECHNIQUE FOR IMPULSE
NOISE REMOVAL
Kartik Sau 1
, Amitabha Chanda 2
, Pabitra Karmakar 3
1
Department of Computer Science & Engineering, Budge Budge Institute of
Technology, Nischintapur. Kolkata 7000137, WBUT, India,
kartik_sau2001@yahoo.co.in
2
Guest faculty, UCSTA, University of Calcutta, West Bengal, India.
amitabha39@yahoo.co.in
3
Department of Computer Science & Engineerin, Institute of Engineering and
Management, WBUT, Salt Lake, Sec –V, Kolkata - 700091, India.
pab_comp@yahoo.co.in
Abstract
Image de-noising is the technique to reduce noises from corrupted images. The aim of the image de-
noising is to improve the contrast of the image or perception of information in images for human viewers
or to provide better output for other automated image processing techniques. This paper presents a new
approach for color image de-noising with Fuzzy Filtering techniques using centroid method for
defuzzification. It preserves any type of edges (including tiny edges) in any direction. The experimental
result shows the effectiveness of the proposed method.
Keywords
Impulse noise; Median filter; Color image de-noising; Defuzzification; Edge Preservation;
1. INTRODUCTION
Color provides a significant portion of visual information to human beings and enhances their
ability of object detection. In the RGB color model, the three primaries are Red, Green, and
Blue. They can be combined, two at a time to create the secondary hues. Magenta is (R+B),
cyan is (B+G), and yellow is (R+G). There are two reasons for using the red, green, and blue
colors as primaries: (1) the cones in the eye are very sensitive to these colors and (2) adding
red, green, and blue can produce many colors although not all colors. For various reasons
digital images are often contaminated with noise at the time of acquisition or transmission. The
noise introduces itself into an image by replacing some of the pixels of the original image by
new pixels having luminance values near or equal to the minimum or maximum of the allowable
dynamic luminance range. Pre-processing of an image is conducted with a view to adjusting
the image for further classification and segmentation. In the process, however, image features
should not be destroyed. This is a difficult task in any image processing system. For this
purpose various types of filters are used. Among those median filter is an important class of
filters. In the present paper we shall discuss some of the median filters.
2. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
34
1.1 Different median filters:
Some important median filters are discussed below in short.
The standard median filter [28,24,8,17,11] : In this filter luminance values in a window are
arranged in an order and the median value is selected. This filter reduces noise reasonably
well, but in the process some information is also lost at low noise densities.
The weighted median filter [30,19,11,10] and the centre-weighted median filter [16,13,25,6,11]
have been proposed to avoid the inherent drawbacks of the standard median filter by
controlling the trade off between the noise suppression and detail preservation.
The switching median filter [31,11] is a type of median filter with an impulse detector. It is so
designed that if the centre pixel is identified by the detector as a corrupted pixel, then it is
replaced with the output of the median filter, otherwise, it is left unchanged. The tri state
median filter [7] and the multi-state median filter (MSMF) [8] are two modification of
switching median filter.
Two other types of switching median filters worth mentioning are progressive switching
median filter (PSMF) [29] and Signal-dependent rank-ordered mean filter (SDROMF) [1,2].
The progressive switching median filter (PSMF) [29] is a derivative of the basic switching
median filter. In this filtering approach, detection and removal of impulse noise are iteratively
done in two separate stages. Despite its improved filtering performance it has a very high
computational complexity due to its iterative nature. Signal-dependent rank-ordered mean filter
(SDROMF) [1,2] uses rank-ordered mean filter.
Adaptive centre weighted median (ACWM) [19,11] filter avoids the drawbacks of the CWM filters
and switching median filter. Input data will be clustered by scalar quantization (SQ) method,
this results in fixed threshold for all of images.
Fuzzy-Median filters of different types have gained lot of importance in image processing.
Some such filters [3,18,27,5,22,15] are worth mentioning.
2. PROPOSED METHOD FOR DE-NOISING
2.1 Phase I
Any given digital RGB color image can be represented by a two dimensional array of size
M×N for each Red, Green and Blue primaries. When we are capturing or transmitting the
images, then there will be some noises due to improper opening and closing of the shutter,
atmospheric turbulence, misfocus of lens, relative motion between camera and objects [12].
For removing the noise from the image we can pursue the following steps:
Step 1: Let the input and output images of median filter are X(i,j) & y1(i,j) respectively. The
median filter uniformly replaces the central pixel of a window (W) by the median of the pixels
bounded by predefined window (W) size w × w. The output of the median filter [26,10,32] is
given by
y1(i,,j) = median { X(i-s; j-t) :(s,t) ε W} (1)
and W= {(s, t): -n≤ s, t ≤ n } (2)
3. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
35
Here we consider n=1
Step 2: The pixel values of the neighborhood pixels of (i,j) of original image are sorted in
some specific order.
Step 3: The Fuzzy membership [14,22,20,17] value is assigned for each pixel in a window of w
× w size using some suitable membership functions. The membership function used here is
given below.
(i) A triangular shaped membership function is used.
(ii) The highest and lowest gray values get the membership value zero.
(iii) Assign the membership value 1 to the mean value of the gray level of the window of size
w×w.
The triangular membership function [14,20,17], also called bell-shaped function with straight
lines, can be defined as follows:
µ(x;α,β,γ) = 0 if x ≤ α
= (x-α)/ (β- α+1) if α < x ≤ β (3)
= (α-x)/ (β- α+1) if β < x ≤ γ
One typical plot of the triangular membership is given in the following figure.
Gray value →
Figure 1: Triangular membership function
Step 4: Now de-fuzzyfy [14,20,21,32] the membership value using the Centroid method by the
following formula, and select the output for that window. Let it be y2(i,j) at the point (i,j) in the window
of size w×w
∑
∑
=
x x
x x
j
i
j
i
x
j
i
y
)
,
(
)
,
(
)
,
(
2
µ
µ
(4)
2.2 Phase II
If we apply the phase I on the noisy images, noise would be removed. But the problem is,
though the noises are represented as 0 and 255, so all 0 and 255 will also be removed; even of
those were not noises. For preserving the actual data we pursues the following steps
4. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
36
Step 1: For the noisy pixel at the point (i,j), we compute p(i,j) and q(i,j) by the following
formulas [21]:
p(i,j)= |f(i,j) – median{Lf(i,j)}|.
Here L(f(i,j)) is the 8-neighborhood of point (i,j).
2
)
,
(
)
,
(
)
,
(
)
,
(
)
,
(
2
1 j
i
fc
j
i
f
j
i
fc
j
i
f
j
i
q
−
+
−
= (5)
Here )
,
(
1 j
i
fc , )
,
(
2 j
i
fc are the closest values of f(i, j) in the filter window of size w×w.
Then rearrange p(i,j) and q(i,j) in ascending order for all i,j =0,1,2.
Step2: Compute w(i,j)= F(p(i,j), q(i,j)) such that
2
1
)
,
(
)
,
(
)
,
(
1
)
,
(
k
j
i
q
j
i
p
k
j
i
p
j
i
w
+
+
+
−
= (6)
Here k1 and k2 are real quantity, which depends on the quality of the input images. Select the
maximum w(i,j).
Step3: If y1(i, j) ≠ y2(i, j)
then ))
,
(
)
,
(
1
(
)
,
(
)
,
(
)
,
( 2
1
3 j
i
y
j
i
w
j
i
y
j
i
w
j
i
y −
+
= (7)
Else y3(i, j) = y1(i, j)
Step 4: Continue this process for all the pixels which are noisy.
The computational procedure is illustrated below.
Example: Consider a color image of size 3×3, as follows,
Table 1: color image with size 3×3
163 193 151 83 113 71 55 81 39
179 0 138 99 0 58 68 0 29
192 172 127 112 92 47 85 63 21
Red Green Blue
5. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
37
For the Green component, we calculate membership values as follows
Original value: 0 Mean value: 75 Median value y1(i,,j): 83
Table 2 : Membership values for pixels
Sorted
order 0
47 58 71 83 92 99 112 113
Member
ship
value
0.00 0.26 0.55 0.89 0.79 0.55 0.37 0.26 0.00
According to Table 2 the graph is as follows –
Gray value →
Figure 2
Now we calculate y2(i,j) using centroid method
Table 3
Sector Area of the sector Centroid of the sector
a 6.184211 23
b 4.486842 52
c 9.407894 64
d 10.105263 77
e 6.039474 87
f 3.223684 95
g 2.565789 105
h 0.013158 112
6. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
38
So, the calculated value for ∑
x x
j
i
x )
,
(
µ = 2858.328857
And the calculated value for ∑
x x
j
i )
,
(
µ = 42.02631
Therefore, )
,
(
2 j
i
y = 68 [Selected value in phase I ]
Rearranged p(i,j) and q(i,j) as follows
Table-4: Different values of p, q and w.
p(i,j) q(i,j) w(i,j)
0 7 0.956522
9 7.5 0.207792
12 8 0.509804
16 10 0.388889
25 10.5 0.388278
29 12 0.328947
30 12.5 0.202703
36 17.5 0.47222
83 52.5 0.330275
Therefore y3(i,j) = 82
Gray value →
Figure 3
The figure 3 shows the membership function for the pixel values of Green component of RGB
image. And also indicate the selected value in the proposed method of Green component.
Now, for the Red component of RGB image, the selected values are as follows
Original value: 0
Mean value: 146
Median value y1(i, j): 163
7. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
39
)
,
(
2 j
i
y = 108 [Selected value in phase I]
And y3(i,j) = 160 [in Phase II]
Gray value →
Figure 4 : Membership function for Red component
The Figure 4 shows the membership function for the pixel values of Red component of RGB
image. And also indicate the selected value in proposed method for RED component.
Similarly, for the Blue component of RGB image, the selected values are as follows
Original value: 0
Mean value: 49
Median value y1(i,,j): 55
)
,
(
2 j
i
y = 47 [Selected value in phase I]
and y3(i,j) = 54
Gray value →
Figure 5: Membership function for Blue component
The Figure 5 shows the membership function for the pixel values of Blue components of RGB
image. And also indicate the selected value in proposed method.
8. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
40
3. FLOW CHART
The flow chart of the proposed method is shown below.
Figure 6
4. EXPERIMENTAL RESULT
The effectiveness of the proposed method is shown experimentally. It eliminates the fixed
value impulse noise. And it is tested for different images in different sizes. The peak signal-to-
noise ratio (PSNR) [19,22,23,27,29,30] value gives the performance of restoration quantitatively,
which is defined as
−
=
∑
∑
k
k
k
y
k
d
PSNR 2
2
10
))
(
)
(
(
255
log
10 (8)
Where 255 is the peak pixel value of the image, d(k) represents the value of the desired output,
and y(k) represents the value of the physical output.
The experimental results were compared with median-filter (MED).
9. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
41
4.1 Original Images
4.2 Noisy Images
10. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
42
4.3 Median Filter Output
4.4 Proposed Filter Output
11. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
43
Table 5: PSNR Comparison Table
Image name PSNR for Median Filter PSNR Proposed filter
lena 27.583501 27.588477
bamboo 25.972569 25.980521
track 25.941161 25.949895
grass 24.008344 24.012961
road 25.584527 25.599688
forest 25.729469 25.743479
wires 23.657962 23.666070
s1 25.216643 25.2242
s2 24.469417 24.473686
s3 24.610376 24.618010
Table 6: Edge Count Comparison Table
Image name Original Image
Median Filter
Output
Proposed Filter
Output
lena 22384 22267 22383
bamboo 32688 32563 32663
track 34686 34468 34576
grass 27381 27224 27244
road 39945 39668 39830
forest 32062 31650 31754
wires 41625 41337 41425
s1 33467 33522 33339
s2 29437 29370 29415
s3 26717 26577 26695
From the Table 5 we observed that the proposed method is better than the median filter due to
better PSNR. From the Table 6 we conclude that more edges [9] be preserved for the proposed
method than median filter.
12. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
44
5. CONCLUTION
In this paper, we propose a new filtering approach for noise removal. It based on soft
computing techniques which can remove the noise and at the same time tiny edges can be
preserved. This enhances the reliability for getting the output image, which is more or less
same with original image. From the Table 5 we conclude that the best PSNR can be achieved
by using the proposed method. The proposed method is tested more than 1000 pictures,
which gives the better result for each test image.
REFERENCE
[1] Abreu E., Lightstone M., Mitra S. K., Arakawa K., “A new efficient approach for the
removal of impulse noise from highly corrupted images,” IEEE Trans Image Processing,
vol. 5, no. 6, pp. 1012-1025, 1996
[2] Abreu E., Mitra S.K., “A signal-dependent rank ordered mean (SD-ROM) filter. A new approach
for removal of impulse From highly corrupted images”, Proceedings of IEEE ICASSP-95,
Detroit, MI, 1995, pp. 2371-2374.
[3] Arakawa K., “Median filters based on fuzzy rules and its application to image restoration”,
Fuzzy Sets and Systems 77 (1996) 3-13.
[4] Baudes, A., B. Coll, Morel J, M, 2005. “A non-local algorithm for noise denoising”, Computer Vision
and Pattern Recognition, CVPR. IEEE Conf., 2: 60-65, 20-25
[5] Bezdek J. C., Pattern Recognition with Fuzzy Objective Function Algorithms. New York:
Plenum, 1981
[6] Chen T.,. Wu H. R., “Adaptive impulse detection using center- weighted median filters,” IEEE
Signal Processing Letters, vol. 8, no. 1, pp. 1–3, 2001.
[7] Chen T,. Ma K. K ,. Chen L.-H, “Tri-state median filter for image denoising”, IEEE Trans. Image
Processing, vol. 8, no. 12, pp. 1834 – 1838, 1999.
[8] Chen. T., Wu H. R., “Space variant median filters for the restoration of impulse noise corrupted
images,” IEEE Trans. on Circuits and Systems II: Analog and Digital Signal processing, vol.
48, no. 8, pp. 784–789, 2001.
[9] Gonzalez. R. C, Digital Image processing. 2nd
Edition TMH
[10] Hamza A., Krim H., “Image denoising A non linear robust statistical approach.” IEEE. Trans. Signal
Processing 49(2), pp. 3045-3054; 2001
[11] Hwang H., Haddad R. A., “Adaptive Median filters: New algorithms and results”. IEEE transaction
on Image processing, 4, pp. 499-502 1995
[12] Jayaraman S., Esakkirajan, S., Veerakumar T, “Digital Image processing”, Tata McGraw Hill
Education Private Limited.
[13] Jeong B. , Lee Y. H., “Design of weighted order statistic filters using the perceptron algorithm,”
IEEE Trans. Signal Processing, vol. 42, no. 11, pp. 3264–3269, 1994.
[14] Konar A, Computational Intelligence, Springer, New York, 2004
13. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
45
[15] Lakshimiprabha S., A new method of image denoising based on fuzzy logic. International Journal of
Soft computing 3(1): 74-77, 2008
[16] Lee Y. H., Ko, . S.-J “Center weighted median filters and their applications to image enhancement,”
IEEE Trans. Circuits and Systems, vol. 38, no. 9, pp. 984 – 993, 1991.
[17] Lee, C. S. V H Kuo, P. T Yu. “Weighted Fuzzy mean filters for Image processing”. Fuzzy Sets Sys.
89, 157-180; 1997
[18] Lin T.-C., Yu P.-T., Partition fuzzy median filter based on fuzzy rules for image restoration, Fuzzy
Sets and Systems 147 (2004) 75 - 97.
[19] Lin T.C., Yu P.-T, “ A new adaptive center weighted median filter for suppressing impulsive noise in
images”, Information Sciences 177 (2007) 1073-1087.
[20] Ross. T. J, Fuzzy logic with Engineering Applications, 2nd edition, University of New Mexico, USA,
Wiley India.
[21] Sadoghi H., Yazdi, Homayouni F., “Impulsive Noise Suppression of Images Using Adaptive Median
Filter”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 3,
No. 3, September, 2010
[22] Sau K., Chanda A.,“Image De-Noising with A Fuzzy Filtering Technique”, India, 2011
[23] Sau K., Chanda A., Karmakar P, “Image De-noising with Edge Preservation Using Soft Computing
Techniques” Annual International Conference on Emerging Research Areas(AICERA). 2011 in
Collaboration with IUPUI United States
[24] Sonka, M., Hlavac V., Boyle R.,Image Processing, Analysis, and Machine Vision, PWS Publishing,
Pacific Grove, Calif, USA, 1999.
[25] Sun T. , Neuvo Y, “Detail-preserving median based filters in image processing,” Pattern
Recognition Letters, vol. 15, no. 4, pp. 341– 347, 1994.
[26] Somasundram K. Shanmugavadivy P. Adaptive iterative order Statistics Filter, ICGST-GVIP journal
Vol. 9 issue 4, pp 23-32, 2009
[27] Smolka B., Chydzinski A., Fast detection and impulsive noise removal in color images, Real-Time
Imaging 11 (2005) 389 - 402.
[28] Umbaugh, S. E. Computer Vision and Image Processing, Prentice-Hall, Englewood Cliffs, NJ, USA,
1998.
[29] Wangand Z., Zhang D., “Progressive switching median filter for the removal of impulse noise
from highly corrupted images,”IEEE Trans. on Circuits and Systems II: Analog and Digital Signal
Processing, vol. 46, no. 1, pp. 78–80, 1999.
[30] Yli-Harja O., Astola J., and Neuvo Y., “Analysis of the properties of median and weighted median
filters using threshold logic and stack filter representation,” IEEE Trans Signal Processing, vol. 39,
no. 2, pp. 395– 410, 1991.
[31] Zhang S.,. Karim M. A, “A new impulse detector for switching median filters,” IEEE Signal
Processing Letters, vol. 9, no. 11, pp. 360–363, 2002
[32] Zimmermann H. J “Fuzzy set theory- And its applications”, second edition, Allied publishers limited.
14. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.3, September 2011
46
AUTHORS
[1] Kartik Sau
Kartik Sau completed his B.Sc. in mathematics from R. K Mission Vidyamandira,
University of Calcutta. And M. Sc. in the same subject from Indian Institute of
Technology, M. Tech in Computer Science from Indian School of Mines,
Dhanbad. Currently he is the Head of the department of Computer Science &
Engineering in Budge Budge Institute of Technology and a member of the
governing body of the same institution. He has presented many papers in
International and National journals and Conferences. His area of interest includes
Digital Image processing, Artificial Intelligence, Pattern Recognition, Soft
computing, etc. He has more than eight years of teaching and research experience
in his area of interest.
[2] Dr. Amitabha Chanda
Dr. Amitabha Chanda: visiting Professor; Department of Computer Science, UCSTA; Calcutta
University. He completed his B.E in Chemical Engineering (Jadavpur), M.A. in Pure Mathematics and
Ph.D in Mathematics from university of Calcutta. He was a faculty member of Indian Statistical Institute
(ISI), Kolkata. Now he is also guest faculty member of ISI, Kolkata; Department of Computer Science,
Rajabazar Science College, Kolkata. His area of interest includes Digital Image processing, Pattern
Recognition Fuzzy logic, Genetic Algorithms, Computer Graphics, Control system Turbulence, Fractal,
multifractals, Clifford Algebra, Nonlinear dynamics. He has more than fifty years teaching and research
experience in his area of interest. He has presented more than 50 papers in International and National
journals and Conference. Dr. Chanda is an Associate member of American Mathematical Society.
[3] Pabitra Karmakar
Pabitra Karmakar received his M. Tech degree in Computer Science &
Engineering from Institute of Engineering & Management, Salt Lake Kolkata,
India. He completed his B. Tech degree in Computer Science & Engineering
from Dumkal Institute of Engineering & Technology, WBUT, India. Also he was
a faculty member of Institute of Engineering and Management in Department of
Computer Science.& Engineering. He have certified in SCJP 1.4 from Sun
Microsystem, USA and MTA certified form Microsoft Corporation. Also he is an
IBM certified C and C++ programmer. Beside this, He has a industry experience
in software development. Currently he is an IT professional. His area of interest
includes Digital Image processing, Pattern Recognition, Fuzzy logic, Genetic
Algorithms, Neural Networks, RDBMS and programming languages.