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.
This document discusses various techniques for image contrast enhancement, including contrast stretching, grey level slicing, histogram equalization, local enhancement equalization, image subtraction, and spatial filtering. It provides details on how each technique works and compares their performance both qualitatively and quantitatively using metrics like SNR and PSNR. The conclusion is that contrast stretching generally provides the best enhancement among the techniques compared, but other techniques may be better suited for specific applications.
An Efficient Approach of Segmentation and Blind Deconvolution in Image Restor...iosrjce
This paper introduces the concept of Blind Deconvolution for restoration of a digital image and
small segments of a single image that has been degraded due to some noise. Concept of Image Restoration is
used in various areas like in Robotics to take decision, Biomedical research for analysis of tissues, cells and
cellular constituents etc. Segmentation is used to divide an image into multiple meaningful regions. Concept of
segmentation is helpful for restoration of only selected portion of the image hence reduces the complexity of the
system by focusing only on those parts of the image that need to be restored. There exist so many techniques for
the restoration of a degraded image like Wiener filter, Regularized filter, Lucy Richardson algorithm etc. All
these techniques use prior knowledge of blur kernel for restoration process. In Blind Deconvolution technique
Blur kernel initially remains unknown. This paper uses Gaussian low pass filter to convolve an image. Gaussian
low pass filter minimize the problem of ringing effect. Ringing effect occurs in image when transition between
one point to another is not clearly defined. After removing these ringing effects from the restored image,
resultant image will be clear in visibility. The aim of this paper is to provide better algorithm that can be helpful
in removing unwanted features from the image and the quality of the image can be measured in terms of
PSNR(Peak Signal-to-Noise Ratio) and MSE(Mean Square error). Proposed Technique also works well with
Motion Blur.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
Ultrasound images and SAR i.e. synthetic aperture radar images are usually corrupted because of speckle
noise also called as granular noise. It is quite a tedious task to remove such noise and analyze those
corrupted images. Till now many researchers worked to remove speckle noise using frequency domain
methods, temporal methods, and adaptive methods. Different filters have been developed as Mean and
Median filters, Statistic Lee filter, Statistic Kuan filter, Frost filter, Srad filter. This paper reviews filters
used to remove speckle noise.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Target Detection Using Multi Resolution Analysis for Camouflaged Images ijcisjournal
Target detection is a challenging problem having many applications in defense and civil. Most of the
targets in defense are camouflaged. It is difficult for a system to detect camouflaged targets in an image. A
novel and constructive approach is proposing to detect object in camouflage images. This method uses
various methodologies such as 2-D DWT, gray level co-occurrence matrix (GLCM), wavelet coefficient
features, region growing algorithm and canny edge detection. Target detection is achieved by calculating
wavelet coefficient features from GLCM of transformed sub blocks of the image. Seed block is obtained by
evaluating wavelet coefficient features. Finally the camouflage object is highlighted using image
processing schemes. The proposed target detection system is implemented in Matlab 7.7.0 and tested on
different kinds of images.
Removal of Unwanted Objects using Image Inpainting - a Technical ReviewIJERA Editor
Image In painting, the technique to change image in undetectable structure, it itself is an ancient art. There are
various goals and applications of image in painting which includes restoration of damaged painting and also to
replace/remove the selected objects. This paper, describes various techniques that can help in removing
unwanted objects from image. Even the in painting fundamentals are directly further, most inpainting techniques
available in the literature are difficult to understand and implement.
A novel embedded hybrid thinning algorithm forprjpublications
The document proposes a hybrid thinning algorithm that combines the Stentiford and Zhang-Suen thinning algorithms. It compares the hybrid algorithm to the original Stentiford and Zhang-Suen algorithms on an input image. The hybrid algorithm more accurately thins the image to a single pixel width but does not improve time complexity compared to the original algorithms. The hybrid approach uses four templates across two sub-iterations to identify and remove pixels based on connectivity values until no more can be removed. Experimental results show the hybrid algorithm more effectively increases image contrast than the original thinning algorithms.
This document discusses various techniques for image contrast enhancement, including contrast stretching, grey level slicing, histogram equalization, local enhancement equalization, image subtraction, and spatial filtering. It provides details on how each technique works and compares their performance both qualitatively and quantitatively using metrics like SNR and PSNR. The conclusion is that contrast stretching generally provides the best enhancement among the techniques compared, but other techniques may be better suited for specific applications.
An Efficient Approach of Segmentation and Blind Deconvolution in Image Restor...iosrjce
This paper introduces the concept of Blind Deconvolution for restoration of a digital image and
small segments of a single image that has been degraded due to some noise. Concept of Image Restoration is
used in various areas like in Robotics to take decision, Biomedical research for analysis of tissues, cells and
cellular constituents etc. Segmentation is used to divide an image into multiple meaningful regions. Concept of
segmentation is helpful for restoration of only selected portion of the image hence reduces the complexity of the
system by focusing only on those parts of the image that need to be restored. There exist so many techniques for
the restoration of a degraded image like Wiener filter, Regularized filter, Lucy Richardson algorithm etc. All
these techniques use prior knowledge of blur kernel for restoration process. In Blind Deconvolution technique
Blur kernel initially remains unknown. This paper uses Gaussian low pass filter to convolve an image. Gaussian
low pass filter minimize the problem of ringing effect. Ringing effect occurs in image when transition between
one point to another is not clearly defined. After removing these ringing effects from the restored image,
resultant image will be clear in visibility. The aim of this paper is to provide better algorithm that can be helpful
in removing unwanted features from the image and the quality of the image can be measured in terms of
PSNR(Peak Signal-to-Noise Ratio) and MSE(Mean Square error). Proposed Technique also works well with
Motion Blur.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
Ultrasound images and SAR i.e. synthetic aperture radar images are usually corrupted because of speckle
noise also called as granular noise. It is quite a tedious task to remove such noise and analyze those
corrupted images. Till now many researchers worked to remove speckle noise using frequency domain
methods, temporal methods, and adaptive methods. Different filters have been developed as Mean and
Median filters, Statistic Lee filter, Statistic Kuan filter, Frost filter, Srad filter. This paper reviews filters
used to remove speckle noise.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Target Detection Using Multi Resolution Analysis for Camouflaged Images ijcisjournal
Target detection is a challenging problem having many applications in defense and civil. Most of the
targets in defense are camouflaged. It is difficult for a system to detect camouflaged targets in an image. A
novel and constructive approach is proposing to detect object in camouflage images. This method uses
various methodologies such as 2-D DWT, gray level co-occurrence matrix (GLCM), wavelet coefficient
features, region growing algorithm and canny edge detection. Target detection is achieved by calculating
wavelet coefficient features from GLCM of transformed sub blocks of the image. Seed block is obtained by
evaluating wavelet coefficient features. Finally the camouflage object is highlighted using image
processing schemes. The proposed target detection system is implemented in Matlab 7.7.0 and tested on
different kinds of images.
Removal of Unwanted Objects using Image Inpainting - a Technical ReviewIJERA Editor
Image In painting, the technique to change image in undetectable structure, it itself is an ancient art. There are
various goals and applications of image in painting which includes restoration of damaged painting and also to
replace/remove the selected objects. This paper, describes various techniques that can help in removing
unwanted objects from image. Even the in painting fundamentals are directly further, most inpainting techniques
available in the literature are difficult to understand and implement.
A novel embedded hybrid thinning algorithm forprjpublications
The document proposes a hybrid thinning algorithm that combines the Stentiford and Zhang-Suen thinning algorithms. It compares the hybrid algorithm to the original Stentiford and Zhang-Suen algorithms on an input image. The hybrid algorithm more accurately thins the image to a single pixel width but does not improve time complexity compared to the original algorithms. The hybrid approach uses four templates across two sub-iterations to identify and remove pixels based on connectivity values until no more can be removed. Experimental results show the hybrid algorithm more effectively increases image contrast than the original thinning algorithms.
IRJET- Performance Analysis of Non Linear Filtering for Image DenoisingIRJET Journal
This document summarizes a research paper that proposes a new approach for image denoising using non-linear filtering. It begins with an introduction to image noise and denoising techniques. It then discusses using wavelet edge detection and non-linear filtering based on thresholding to enhance noisy images. The methodology applies wavelet transformation to identify corrupted regions, uses a "swarm filter" to replace pixel values in that region with similar values from uncorrupted areas. Results show this approach improves PSNR and lowers MSE compared to existing edge detection and wavelet transform methods, better preserving image features while reducing noise.
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.
A Review of different method of Medical Image Segmentationijsrd.com
Image Segmentation is a most important task of image analysis. Number of method used for image segmentation. Image segmentation mainly used in different field like medical image analysis, character re-congestion etc. A segmentation method finds the sets that are different structure from each other and completion of segmentation process that cover entire image.
Image deblurring based on spectral measures of whitenessijma
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
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.
A survey on efficient no reference blur estimation methodseSAT Journals
Abstract Blur estimation in image processing has come to be of great importance in assessing the quality of images. This work presents a survey of no-reference blur estimation methods. A no-reference method is particularly useful, when the input image used for blur estimation does not have an available corresponding reference image. This paper provides a comparison of the methodologies of four no-reference blur estimation methods. The first method applies a scale adaptive technique of blur estimation to get better accuracy in the results. The second blur metric involves finding the energy using second order derivatives of an image using derivative pair of quadrature filters. The third blur metric is based on the kurtosis measurement in the discrete dyadic wavelet transform (DDWT) of the images. The fourth method of blur estimation is obtained by finding the ratio of sum of the edge widths of all the detected edges to the total number of edges. The results provided are useful in comparing the methods based on metrics like Spearman correlation coefficient. The results are obtained by evaluation on images from the Laboratory for Image and Video Engineering (LIVE) database. The various methods are evaluated on the images by adding varying content of noise. The performance is evaluated for 3 different categories namely Gaussian blur, motion blur and also JPEG2000 compressed images. Blur estimation finds its application in quality assessment, image fusion and auto-focusing in images. The sharpness of an image can also be found from the blur metric as sharpness is inversely proportional to blur. Sharpness metrics can also be combined with other metrics to measure overall quality of the image. Keywords: Blur estimation, blur, no-reference
This document summarizes and compares four no-reference blur estimation methods: (1) a scale adaptive wavelet transform method, (2) a method using energy of quadrature filters, (3) a kurtosis measurement method using discrete dyadic wavelet transform, and (4) a method measuring average edge width. The methods are evaluated on blurred, noisy, and compressed images from a database. Results show the quadrature filter method performs best except for compressed images, where a perceptual method works best. The scale adaptive and kurtosis methods degrade more with noise while the quadrature filter method is more robust.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
Segmentation and Classification of MRI Brain TumorIRJET Journal
This document presents a study comparing two techniques for detecting brain tumors in MRI images: level set segmentation and K-means segmentation. Features are extracted from the segmented tumors using discrete wavelet transform and gray level co-occurrence matrix. The features are then classified as benign or malignant using a support vector machine. The level set method and K-means method are evaluated based on accuracy, sensitivity, and specificity on a dataset of 41 MRI brain images. The level set method achieved slightly higher accuracy of 94.12% compared to the K-means method.
Gaussian noise reduction on images automaticallyeSAT Journals
Abstract
The various images can be taken by camera, due to some artifacts image become noisy and blur .As a result the image may be containing the noise and unwanted details. This noise will be degrading the image information. The proposed method also used removes the noise in images. These methods are using combined total variation and wavelet thresholding to removing the noise from images. The image quality can be measured using the MSE (Mean Square Error),PSNR (Peak Signal to Noise Ratio),Entropy, Correlation Coefficient.
Keywords: Noise, Total variation, PSNR, MSE.
Detection of hard exudates using simulated annealing based thresholding mecha...csandit
This document presents a method for detecting hard exudates in retinal fundus images using simulated annealing based thresholding. The proposed method involves 5 steps: 1) median filtering to reduce noise and blur exudates, 2) image subtraction between the input and filtered images to extract bright regions, 3) application of simulated annealing to determine an optimal threshold value, 4) thresholding with the determined value to segment exudates, and 5) image addition to enhance detection. The method was tested on 10 images and achieved an overall sensitivity of 98.66% and predictivity of 98.12% according to expert evaluation, demonstrating accurate exudate detection.
Instant fracture detection using ir-raysijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comprehensive Study of the Work Done In Image Processing and Compression Tech...IRJET Journal
This document summarizes research on image processing techniques to address redundancy. It discusses how overlapping pixels when merging images can cause redundancy, taking up extra space. It reviews papers analyzing redundancy problems from compression techniques. Lossy techniques like discrete cosine transform and lossless techniques like run length encoding and Huffman encoding are described for compressing images to reduce redundancy. The document also discusses using compression to eliminate irrelevant information from images.
Wavelet transformation based detection of masses in digital mammogramseSAT Publishing House
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
Wavelet transformation based detection of masses in digital mammogramseSAT Journals
This document describes a wavelet transformation-based method for detecting masses in digital mammograms. The method uses wavelet analysis to highlight variations in intensities that may indicate masses. It applies preprocessing techniques like median filtering to reduce noise and morphological operations to remove the pectoral muscle and suppress artifacts. Region properties and seeded region growing are then used to accurately segment abnormal masses. The combined use of wavelet transformation and region growing enables effective mass segmentation, demonstrating the effectiveness of the proposed technique. The method is tested on over 30 mammograms and shows improvements over traditional mass detection approaches.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
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.
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.
A Novel Framework For Preprocessing Of Breast Ultra Sound Images By Combining...IRJET Journal
The document presents a novel framework for preprocessing breast ultrasound images that combines non-local means filtering and morphological operations. Non-local means filtering is used to reduce speckle noise, which is a significant issue for ultrasound images. Then morphological techniques are applied to enhance the noise-reduced images. The framework achieves peak signal-to-noise ratios of 60-80 decibels when tested on real breast ultrasound images. It provides an effective method for preprocessing ultrasound images to reduce noise and improve image quality.
Comparison on average, median and wiener filter using lung imagesIRJET Journal
This document compares the performance of average, median, and Wiener filters for removing noise from lung images. It analyzes the filters using peak signal-to-noise ratio (PSNR), mean square error (MSE), and root mean square error (RMSE) on 6 lung images with added noise. The results show that the Wiener filter produces the best denoising performance with the highest PSNR and lowest MSE and RMSE values. It is concluded that the Wiener filter is the best approach for removing noise from lung images compared to average and median filters.
Performance analysis of image filtering algorithms for mri imageseSAT Publishing House
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
IRJET- Performance Analysis of Non Linear Filtering for Image DenoisingIRJET Journal
This document summarizes a research paper that proposes a new approach for image denoising using non-linear filtering. It begins with an introduction to image noise and denoising techniques. It then discusses using wavelet edge detection and non-linear filtering based on thresholding to enhance noisy images. The methodology applies wavelet transformation to identify corrupted regions, uses a "swarm filter" to replace pixel values in that region with similar values from uncorrupted areas. Results show this approach improves PSNR and lowers MSE compared to existing edge detection and wavelet transform methods, better preserving image features while reducing noise.
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.
A Review of different method of Medical Image Segmentationijsrd.com
Image Segmentation is a most important task of image analysis. Number of method used for image segmentation. Image segmentation mainly used in different field like medical image analysis, character re-congestion etc. A segmentation method finds the sets that are different structure from each other and completion of segmentation process that cover entire image.
Image deblurring based on spectral measures of whitenessijma
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
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.
A survey on efficient no reference blur estimation methodseSAT Journals
Abstract Blur estimation in image processing has come to be of great importance in assessing the quality of images. This work presents a survey of no-reference blur estimation methods. A no-reference method is particularly useful, when the input image used for blur estimation does not have an available corresponding reference image. This paper provides a comparison of the methodologies of four no-reference blur estimation methods. The first method applies a scale adaptive technique of blur estimation to get better accuracy in the results. The second blur metric involves finding the energy using second order derivatives of an image using derivative pair of quadrature filters. The third blur metric is based on the kurtosis measurement in the discrete dyadic wavelet transform (DDWT) of the images. The fourth method of blur estimation is obtained by finding the ratio of sum of the edge widths of all the detected edges to the total number of edges. The results provided are useful in comparing the methods based on metrics like Spearman correlation coefficient. The results are obtained by evaluation on images from the Laboratory for Image and Video Engineering (LIVE) database. The various methods are evaluated on the images by adding varying content of noise. The performance is evaluated for 3 different categories namely Gaussian blur, motion blur and also JPEG2000 compressed images. Blur estimation finds its application in quality assessment, image fusion and auto-focusing in images. The sharpness of an image can also be found from the blur metric as sharpness is inversely proportional to blur. Sharpness metrics can also be combined with other metrics to measure overall quality of the image. Keywords: Blur estimation, blur, no-reference
This document summarizes and compares four no-reference blur estimation methods: (1) a scale adaptive wavelet transform method, (2) a method using energy of quadrature filters, (3) a kurtosis measurement method using discrete dyadic wavelet transform, and (4) a method measuring average edge width. The methods are evaluated on blurred, noisy, and compressed images from a database. Results show the quadrature filter method performs best except for compressed images, where a perceptual method works best. The scale adaptive and kurtosis methods degrade more with noise while the quadrature filter method is more robust.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
Segmentation and Classification of MRI Brain TumorIRJET Journal
This document presents a study comparing two techniques for detecting brain tumors in MRI images: level set segmentation and K-means segmentation. Features are extracted from the segmented tumors using discrete wavelet transform and gray level co-occurrence matrix. The features are then classified as benign or malignant using a support vector machine. The level set method and K-means method are evaluated based on accuracy, sensitivity, and specificity on a dataset of 41 MRI brain images. The level set method achieved slightly higher accuracy of 94.12% compared to the K-means method.
Gaussian noise reduction on images automaticallyeSAT Journals
Abstract
The various images can be taken by camera, due to some artifacts image become noisy and blur .As a result the image may be containing the noise and unwanted details. This noise will be degrading the image information. The proposed method also used removes the noise in images. These methods are using combined total variation and wavelet thresholding to removing the noise from images. The image quality can be measured using the MSE (Mean Square Error),PSNR (Peak Signal to Noise Ratio),Entropy, Correlation Coefficient.
Keywords: Noise, Total variation, PSNR, MSE.
Detection of hard exudates using simulated annealing based thresholding mecha...csandit
This document presents a method for detecting hard exudates in retinal fundus images using simulated annealing based thresholding. The proposed method involves 5 steps: 1) median filtering to reduce noise and blur exudates, 2) image subtraction between the input and filtered images to extract bright regions, 3) application of simulated annealing to determine an optimal threshold value, 4) thresholding with the determined value to segment exudates, and 5) image addition to enhance detection. The method was tested on 10 images and achieved an overall sensitivity of 98.66% and predictivity of 98.12% according to expert evaluation, demonstrating accurate exudate detection.
Instant fracture detection using ir-raysijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comprehensive Study of the Work Done In Image Processing and Compression Tech...IRJET Journal
This document summarizes research on image processing techniques to address redundancy. It discusses how overlapping pixels when merging images can cause redundancy, taking up extra space. It reviews papers analyzing redundancy problems from compression techniques. Lossy techniques like discrete cosine transform and lossless techniques like run length encoding and Huffman encoding are described for compressing images to reduce redundancy. The document also discusses using compression to eliminate irrelevant information from images.
Wavelet transformation based detection of masses in digital mammogramseSAT Publishing House
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
Wavelet transformation based detection of masses in digital mammogramseSAT Journals
This document describes a wavelet transformation-based method for detecting masses in digital mammograms. The method uses wavelet analysis to highlight variations in intensities that may indicate masses. It applies preprocessing techniques like median filtering to reduce noise and morphological operations to remove the pectoral muscle and suppress artifacts. Region properties and seeded region growing are then used to accurately segment abnormal masses. The combined use of wavelet transformation and region growing enables effective mass segmentation, demonstrating the effectiveness of the proposed technique. The method is tested on over 30 mammograms and shows improvements over traditional mass detection approaches.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
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.
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.
A Novel Framework For Preprocessing Of Breast Ultra Sound Images By Combining...IRJET Journal
The document presents a novel framework for preprocessing breast ultrasound images that combines non-local means filtering and morphological operations. Non-local means filtering is used to reduce speckle noise, which is a significant issue for ultrasound images. Then morphological techniques are applied to enhance the noise-reduced images. The framework achieves peak signal-to-noise ratios of 60-80 decibels when tested on real breast ultrasound images. It provides an effective method for preprocessing ultrasound images to reduce noise and improve image quality.
Comparison on average, median and wiener filter using lung imagesIRJET Journal
This document compares the performance of average, median, and Wiener filters for removing noise from lung images. It analyzes the filters using peak signal-to-noise ratio (PSNR), mean square error (MSE), and root mean square error (RMSE) on 6 lung images with added noise. The results show that the Wiener filter produces the best denoising performance with the highest PSNR and lowest MSE and RMSE values. It is concluded that the Wiener filter is the best approach for removing noise from lung images compared to average and median filters.
Performance analysis of image filtering algorithms for mri imageseSAT Publishing House
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
Performance analysis of image filtering algorithms for mri imageseSAT Publishing House
This document analyzes the performance of three image filtering algorithms (median filter, Wiener filter, and center weighted median filter) at removing noise from MRI images. The algorithms are tested on MRI images corrupted with different noise types. The Wiener filter is found to reconstruct images with the highest quality according to measurements of mean square error and peak signal-to-noise ratio. The study concludes the Wiener filter provides the best denoising of MRI images compared to the other algorithms tested.
IRJET- An Efficient Brain Tumor Detection System using Automatic Segmenta...IRJET Journal
This document presents a proposed method for an efficient brain tumor detection system using automatic segmentation with convolutional neural networks. The proposed method uses median filtering for noise removal, Otsu's thresholding for segmentation, and morphological operations for filtering. A convolutional neural network is then used for tumor classification. The methodology is tested on a brain MRI dataset, with evaluations of performance metrics like accuracy, precision, recall, and processing time. The goal is to develop an automated system for early detection of brain tumors using deep learning techniques for analysis of medical images.
Multimodality medical image fusion using improved contourlet transformationIAEME Publication
1. The document presents a technique for medical image fusion using an improved contourlet transformation with log Gabor filters.
2. It proposes decomposing images using a contourlet transformation with modified directional filter banks that incorporate log Gabor filters. This aims to provide high quality fused images while localizing features accurately and minimizing noise.
3. Experimental results on fusing medical images show that the proposed technique achieves higher quality measurements like PSNR compared to a basic contourlet transformation fusion approach.
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.
Abstract: These days analysing patient data in the form of medical images to perform diagnose while doing detection
and prediction of a disease has emerged as a biggest research challenge. All these medical images can be in the form of
X-RAY, CT scan, MRI, PET and SPECT. These images carry minute information about heart, brain, nerves etc within
themselves. It may happen that these images get corrupted due to noise while capturing them. This makes the complete
image interpretation process very difficult and inaccurate. It has been found that the accuracy rate of existing method is
very less so improvement is required to make them more accurate. This paper proposes a Machine Learning Model based
on Convolutional Neural Network (CNN) that will contain all the filters required to de-noise MRI or USI Images. This
model will have same error rate efficiency like those of data mining techniques which radiologists were interested in. The
filters used in the proposed work are namely Weiner Filter, Gaussian Filter, Median Filter that are capable of removing
most common noises such as Salt and Pepper, Poisson, Speckle, Blurred, Gaussian existing in MRI images in Grey Scale
and RGB Scale.
Keywords: Convolution Neural Network, Denoising, Machine Learning, Deep Learning, Image Noise, Filters
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.
Interactive liver tumor segmentation using eSAT Journals
Abstract This literature review attempts to provide a brief overview of the most common segmentation techniques, and a comparison between them. It discusses the “Grab-Cut” technique, and" Graph Cut" techniques. GrabCut is a way to perform 2D segmentation in an image that is very user friendly. The user only need to input a very rough segmentation between foreground and background .The Graph Cut approaches to segmentation can be extended to 3-D data and can be used for segmenting 3-D volumes. Other segmentation techniques use either contour or edge segmentation to perform segmentation. The Graph Cut techniques use both contour and edge detection. Typically this is down by drawing a rectangle around the object of interest. The way that this is accomplished technically is by using a combination of Graph Cuts and statistical models of the foreground and background structure in the colour space. Grab Cut Technique use very minimum energy to separate Foreground and Background Images. Keywords - Interactive Image Segmentation, Object Selection, Foreground extraction, Graph Cut, Grab cut
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.
IRJET- Analytical Study of Various Filters in Lung CT ImagesIRJET Journal
This document analyzes and compares the performance of various filters for preprocessing lung CT images. It first provides background on lung cancer and the importance of medical image processing and preprocessing. It then describes several common filters - median, average, and Wiener - and the methodology used to apply each filter to sample lung CT images. The performance of each filter is evaluated using peak signal-to-noise ratio (PSNR) and mean squared error (MSE) metrics. The results show that the Wiener filter produced better outcomes in terms of lower MSE and higher PSNR values, indicating it is effective at removing noise while preserving image detail. Therefore, the study concludes the Wiener filter is best for preprocessing lung CT images prior to further analysis.
Automatic Detection of Radius of Bone FractureIRJET Journal
This document presents a proposed algorithm for automatically detecting the radius of bone fractures in x-ray images. The algorithm involves several steps: image preprocessing using filters to reduce noise, segmentation using FCM clustering to separate bone regions, feature extraction using Hough transform to identify lines and circles, and detecting the radius of fractures based on the extracted features. The algorithm was tested on 20 x-ray images and achieved about 90% accuracy in detecting fracture radii. The proposed method provides an efficient and accurate approach for fracture detection compared to other methods. Future work may focus on enhancing the algorithm to handle multiple fractures and different image modalities like CT and MRI.
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.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Comparative performance analysis of segmentation techniquesIAEME Publication
This document compares the performance of several image segmentation techniques: global thresholding, adaptive thresholding, region growing, and level set segmentation. It applies these techniques to medical and synthetic images corrupted with noise and evaluates the segmentation results using binary classification metrics like sensitivity, specificity, accuracy, and precision. The results show that level set segmentation best preserves object boundaries, adaptive thresholding captures most image details, and global thresholding has the highest success rate at extracting regions of interest. Overall, the study aims to determine the optimal segmentation method for medical images from CT scans.
Techniques of Brain Cancer Detection from MRI using Machine LearningIRJET Journal
The document discusses techniques for detecting brain cancer from MRI scans using machine learning. It first provides background on brain tumors and MRI. It then outlines the cancer detection process, including pre-processing the MRI data, segmenting the images, extracting features, and classifying tumors using techniques like CNNs, SVMs, MLP, and Naive Bayes. The document reviews related work applying these techniques and compares their results, finding accuracy can be improved with larger, higher resolution datasets.
A Novel Approach for Edge Detection using Modified ACIES Filteringidescitation
The most important humiliating attribute of an image is noise, which may
become obvious during image capturing, communication or processing and may conceivably
be reliant on or sovereign of image. In order to supplement the high- frequency components
in this paper we are proposing a new class of filter called ACIES filter which implies spatial
filter shape that has a high positive component at the centre. Since restraint of noise can
only be achieved by smoothing the image. Sharpening with ACIES filter highlights the fine
details of an image and enhances the clarity of the image at the boundaries. Experimental
results show that the concert of the proposed ACIES filter is acceptable and Quality of the
consequent images is remarkably well even under the strongly noise-corrupted conditions.
If the edges in an image can be recognized specifically, then all the objects in the image can
be located and basic properties such as region, edge, and contour can be measured. The
performance of the proposed approach is measured with image quality metrics such as
PSNR, MSE, NAE and NK.
Similar to A parallel rough set based smoothing filter (20)
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2. International Journal of Computer Science and Engineering Research and Development (IJCSERD),
ISSN 2248-9363 (Print), ISSN 2248-9371 (Online) Volume 1, Number 2, May-October (2011)
theory of rough sets is traditionally formulated based on an equivalence relation on an
object set called the universe. The basic idea of rough set theory is problem solving.
As image contains much uncertainty information, many efforts have been made to use
fuzzy set theory to process image. The results have improved better effect than hard
computing method. Rough sets has potential application for processing image,
Recently, Rough sets theoretic image processing is concerned, but there is less
investigation reported so far on the application of Rough set theory in image
processing. Edge enhancement techniques falls under two categories smoothening
filters and sharpening filters. Smoothing filters are used for blurring and for noise
reduction. Blurring is used in pre-processing steps, such as removal of small details
from an image prior to object extraction, and bridging of small gaps in lines or curves.
Noise reduction can be accomplishing by blurring with a linear filter and also by
nonlinear filtering such as mean, median, mode, circular, pyramidal and cone filters.
Sharpening filters are used to highlight fine detail in an image or to enhance detail
that has been blurred. These filters include Laplacian, Sobel, Prewitt and Robert
filters which are widely used in applications but because of their results of complexity
and image quality, smoothening filters are used which involves simple subtractive
smoothened image concept which reduces complexity and makes the images look
sharper than they really are.
2. DIFFERENT TYPES OF SMOOTHING FILTERS
2.1 What is edge enhancement?
Edge Enhancement is a digital image processing filter that is used to make pictures
look artificially sharper than they really are. The key word here is looking sharper,
because the picture isn't really any more detailed than before. The human eye is
simply tricked into thinking the picture is sharper.
2.2 Smoothing filters:
Mean filter: The mean filter is a simple sliding-window spatial filter that replaces the
center value in the window with the average (mean) of all the pixel values in the
window [2]. The window, or kernel, is usually square but can be any shape. An
example of mean filtering of a single 3x3 window of values is shown below.
5 + 3 + 6 + 2 + 1 + 9 + 8 + 4 + 7 = 45
unfiltered values
45 / 9 = 5
5 3 6 mean filtered
2 1 9 * * *
8 4 7 * 5 *
* * *
Center value is replaced by the mean of all nine values.
Median filter
The median filter is also a sliding-window spatial filter, but it replaces the center
value in the window with the median of all the pixel values in the window. As for the
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3. International Journal of Computer Science and Engineering Research and Development (IJCSERD),
ISSN 2248-9363 (Print), ISSN 2248-9371 (Online) Volume 1, Number 2, May-October (2011)
mean filter, the kernel is usually square but can be any shape [2]. An example of
median filtering of a single 3x3 window of values is shown below.
unfiltered values
6 2 0
3 97 4
19 3 10
In order:
0, 2, 3, 3, 4, 6, 10, 15, 97
median filtered
* * *
* 4 *
* * *
Center value is replaced by the median of all nine values.
Mode Filter
The mode filter replaces the pixel at the centre of the mask by the mode of all the
pixel values in the mask. The mode value is nothing but the maximally repeated value
in the mask.
Circular Filter
In this filter, we will convolute the image the mask provided [1, 3]. This filter is
slightly different from the mean filter [2]. The filter is shown below
Circular Filter Mask
Triangular filter
In this, the output image is based on a local averaging of the input filter, where the
values within the filter support have differing weights. In general, the filter can be
seen as the convolution of two identical uniform filters either mean or circular and
this has the direct consequence for computational complexity. Transfer functions of
these filters do not have the negative values and hence it will not exhibit the phase
reversal. There are two filters of this kind, namely Pyramidal filter and Cone filter.
The convolution masks for these are shown below.
Cone Filter Mask Pyramidal Filter Mask
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4. International Journal of Computer Science and Engineering Research and Development (IJCSERD),
ISSN 2248-9363 (Print), ISSN 2248-9371 (Online) Volume 1, Number 2, May-October (2011)
Medical images
Medical i m a ge analysis poses a far tougher challenge.[3][9] First, there is an
even greater need for image filtering, because medical images have a poorer
noise-to-signal ratio than scenes taken with a digital camera, the spatial
resolution is often frustratingly low, the contrast between anatomically distinct
structures is often too low to be computed reliably using a standard image
processing technique, and artefacts are common (e.g. motion and bias field in
MRI). Second, changes to image content must be done in a highly controlled
and reliable way that does not compromise clinical decision-making. For
example, whereas it is generally acceptable to filter out local bright patches of
noise, care must be taken in the case of mammography not to remove
microcalcifications.This paper briefly explores some of the key areas of
development in the area of filtering in Medical Imaging and how these
techniques impact generally available software packages in routine use in a
diagnostic setting.
3. IMPLEMENTATION
This model uses a parallel structure. The filters are arranged in parallel and the results
are obtained independent of each other which are the passed to an array as shown in
fig 3.1. This array is then sent to the processor where in the image enhancement mean
is generated
Smoothing filters
Application of Filter1
Rough set theory
Filter2
Separation
Processor
ACTUAL of noise
Filter3 (Image
IMAGE pixels from
averaging)
an image
Filter4 Enhanced
Image
Filter N
Fig 3.1 A hybrid smoothing filter using Rough sets
The advantage of this model is that it is time efficient as the calculation of each filter is done
in parallel.
However the limitations are: The system requires all the filters to be available at the same
time.
1. The system has to wait for the result of each independent filter to calculate the mean
Total time taken =Separation of noise pixels using Rough set + Max time of slowest filter +
Processing time.
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5. International Journal of Computer Science and Engineering Research and Development (IJCSERD),
ISSN 2248-9363 (Print), ISSN 2248-9371 (Online) Volume 1, Number 2, May-October (2011)
The available free time of the filters waiting for completion of processing of slowest filter can
be used to update image which can improve the time efficiency.
Algorithm based on Rough sets using smoothing filter
When we separate image noise with Rough set theory, the equivalence relation R can be
defined in many ways.[12] One algorithm is to calculate the grey value margin between the
mean of all pixels. Then, compared with the pre-defined threshold, the pixel whose margin is
greater than the threshold is considered a noise pixel. Another algorithm to classify pixels is
according to the relationship between the pixels value and the maximum and minimum
values in its neighbourhood, the pixel whose value is greater than maximum value or less
than the minimum is regarded as a noise. Both algorithms are focused on single pixel, more
processing time consuming to obtain all pixels mean value or extreme within a
neighbourhood. In the algorithm, the algorithm may take non-noise pixels as noise pixels.
To reduce the time of the noise detection and the possibility of misjudgement of the
noise points, in this paper we divide the image into several parts with the size, then setup the
indiscernible relation of Rough sets and divide sets to separate noise pixels from the normal
ones, finally process the noise pixels with the filters for smoothing the image.
Let U denotes an image that has L gray-level, and its size is M x N. Divide U into S
parts, let Ak(k= 0,1,……S-1) denotes one part of U and f(i,j) denotes the grey value of the
pixel point (i,j). the set of all pixels grey-value in Ak can be defined as formula
Uk = {f(i,j) | (i,j) Ak,k=0,1,…….S-1}
Ak can be seen as a knowledge system, let k=(Ak,R) denotes the approximation space
that is made up with Ak and equivalence relation R. As the impulse noise points grey-value
usually far more or far less than the others, therefore, in a local area, the points whose gray
value close to the maximum or minimum value may be noise points. Suppose that pixel x is
an object of Ak and its grey-value is f(x), let max f(xt) denotes minimum, noise pixels can be
separated by the equivalence relation based on the description of the noise threshold Q
Divide image Ak by a duality equivalence relation R as formula
Ak|R={c1,c2}
Where c1 is the set of all noise pixels and c2is the set of all normal pixels they can be
defined as
C1={|f(x)-max f(xt)|<Q or | f(x)-min f(xt)|< Q} (1 t M)
C2={|f(x)-max f(xt)| Q and | f(x)-min f(xt)| Q} (1 t M)
Where Q is initialise threshold
According to the classification of image based on Rough sets theory, an algorithm
will be available as follows
(1) To divide the orginal image into S parts, the kth (k=0,1,…..S-1) region is marked as Ak.
(2) Such the maximum grey-value max f(xt) and the minimum grey-value min f(xt) in Ak(size
Mk x Nk).
(3) If any pixel x0 in the set Ak can satisfy the condition | f(x0-max f(xt)|<Q or | f(x0)-min
f(xt)|<Q, operate step (4), else operate step (5)
(4) To process the pixels with all smoothing filters.
(5) Not to process the pixels but keep them invariable.
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6. International Journal of Computer Science and Engineering Research and Development (IJCSERD),
ISSN 2248-9363 (Print), ISSN 2248-9371 (Online) Volume 1, Number 2, May-October (2011)
Noisy image
Fig: Image after applying different smoothing filters. In the clockwise direction from
top left are original image, mean filter output, median, pyramidal, circular and mode
4. FUNCTION DONE BY THE PROCESSOR
Image Averaging [6]
Suppose noise η(r, c) is a zero mean pair wise uncorrelated. Then a set of n noisy images
{gi(r, c)} can be given by
gi (r, c) =f(r, c) + ηi(r, c)
Also suppose that ηi (r, c) follows the same distribution for all i, ση2(r, c) be its variance. The
assumptions are approximately valid if we consider, say, transmission channel noise only.
That means if an image f(r, c) is transmitted n times over some communication channel we
may receive a set of noisy images {g1(r, c), g2(r, c),….., gn(r, c)} at the receiver end. The
objective is to recover f(r, c) from the given set {gi(r, c)}. By averaging n such images we get
g(r,c)= (1/n) ∑ni=0 gi(r,c)=f(r,c) + (1/n) ∑ni=0 ηi(r,c)=f(r,c) + η(r,c)
For all r and c. Since noise has zero mean, for large n, g(r, c) approaches f(r, c) and
2 2 2
σ η(r, c) = σ g(r, c) = (1/n) σ η(r,c)
tends to zero as n increases.
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7. International Journal of Computer Science and Engineering Research and Development (IJCSERD),
ISSN 2248-9363 (Print), ISSN 2248-9371 (Online) Volume 1, Number 2, May-October (2011)
APPLICATION OF IMAGE AVERAGING
The main application of image averaging is noise removal. Image noise is mostly unwanted
and manifested in the pixels of an image.[11] It is inherent to digital cameras and is
generated, in part, by heat and low light conditions, and is often prominent in long exposures
and photographs taken at high ISO sensitivity. Its effect is analogous to film grain. When
images of an unchanging scene are corrupted by random noise, a sequence of these images
can be averaged together in order to reduce the effects of the noise. This works because noise
perturbs pixel grey levels, and a positive perturbation of a given magnitude tends to be just as
likely as a negative perturbation of the same magnitude. Hence there is a tendency for these
'errors' in pixel grey level to cancel each other out to an increasing degree, as the number of
averaged images increases.
5. NOISE REDUCTION BY IMAGE AVERAGING
CONCEPT
Image averaging works on the assumption that the noise in your image is truly
random. This way, random fluctuations above and below actual image data will gradually
even out as one average more and more images.[6] If you were to take two shots of a smooth
gray patch, using the same camera settings and under identical conditions (temperature,
lighting, etc.), then you would obtain images similar to those shown on the left.
The above plot represents luminance fluctuations along thin blue and red strips of pixels in
the top and bottom images, respectively. The dashed horizontal line represents the average,
or what this plot look like if there were zero noise. Note how each of the red and blue lines
uniquely fluctuates above and below the dashed line. If we were to take the pixel value at
each location along this line, and average it with value for the pixel in the same location for
the other image, then the luminance variation would be reduced as follows:
Even though the average of the two still fluctuates above and below the mean, the maximum
deviation is greatly reduced. Visually, this has the affect of making the patch to the left
appear smoother. Two averaged images usually produce noise comparable to an ISO setting
which is half as sensitive, so two averaged images taken at ISO 400 are comparable to one
image taken at ISO 200, and so on. In general, magnitude of noise fluctuation drops by the
square root of the number of images averaged, so you need to average 4 images in order to
cut the magnitude in half.
Note how averaging both reduces noise and brings out the detail for each region. Noise
reduction programs such as Neat Image are the best available arsenal against noise, and so
this is used as the benchmark in the following comparison:
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8. International Journal of Computer Science and Engineering Research and Development (IJCSERD),
ISSN 2248-9363 (Print), ISSN 2248-9371 (Online) Volume 1, Number 2, May-October (2011)
A noise free image after image averaging
6. CONCLUSION
By analysing the above Rough set theory approach provides better image quality with
smoothening filters and also provides the best results for displaying the output image. This
new method is compared against all the smoothening filters presented in the paper. By using
Image averaging noise is removed more efficiently.
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