In this paper, we present the performance analysis of adaptive bilateral filter by pixel to noise ratio and mean square errors. It was evaluate changing the parameters of the adaptive filter half width values and standard deviations. In adaptive bilateral filter, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The variance of range filter can also be adaptive. The filter is applied to improve the sharpens of a gray level and color image by increasing the slope of the edges without producing overshoot or undershoots. The related graphs were plotted and the best filter parameters are obtained.
Icamme managed brightness and contrast enhancement using adapted histogram eq...Jagan Rampalli
This document describes a new method called Controlled Contrast Modified Histogram Equalization (CCMHE) to enhance image contrast while managing brightness. CCMHE divides the input image histogram into four sub-histograms based on the median brightness value. It then applies a clipping process to prevent over-enhancement before independently equalizing each sub-histogram. CCMHE also introduces an enhancement rate parameter to control the level of contrast adjustment in the output images. The proposed method aims to produce enhanced images with improved contrast and maintained overall brightness compared to other contemporary enhancement techniques.
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Improved nonlocal means based on pre classification and invariant block matchingIAEME Publication
One of the most popular image denoising methods based on self-similarity is called nonlocal
means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings,
e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable
candidates for some non repetitive patches. In this paper, we propose to improve NLM by integrating
Gaussian blur, clustering, and row image weighted averaging into the NLM framework.
Experimental results show that the proposed technique can perform denoising better than the original
NLM both quantitatively and visually, especially when the noise level is high.
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 adaptive method for noise removal from real world imagesIAEME Publication
The document summarizes an adaptive method for noise removal from real world images. It proposes modifying the bilateral filter, which considers both spatial and intensity distances between pixels. The modified filter adapts its strength based on the local noise level in the image. It estimates the smoothing parameter by analyzing noise strength factors within blocks of different sizes. This helps determine the appropriate block size to use for a given image region. The filter aims to remove Gaussian noise while preserving edges and details to enhance image quality. Experimental results show it performs well across different images for a wide range of noise levels.
This document describes an image fusion method using pyramidal decomposition. It proposes extracting fine details from input images using guided filtering and fusing the base layers of images across multiple exposures or focal points using a multiresolution pyramid approach. A weight map is generated considering exposure, contrast, and saturation to guide the fusion of base layers. The fused base layer is then combined with extracted fine details to produce a detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions of the input images. It is argued that this method can effectively fuse images from different exposures or focal points without introducing artifacts.
www.ijera.com 68 | P a g e Leaf Disease Detection Using Arm7 and Image Proces...IJERA Editor
In an agricultural field plant diseases are very important aspect as it directly affect on the production of plant and economical value of market. In this research generally we uses image processing technique that is automatically detect symptoms of the disease as early as possible. This is the first and important phase for automatic detection and classification of plant diseases. There are some stages to find the disease like image acquisition, preprocessing on image, color transform usingYCbCr, segmentation using Otsu method, feature extraction using Gabor filter method and classification using SVM, using those steps we can surely detect the disease and classified it and also can take preventive measures.
This document discusses techniques for enhancing and analyzing thermal images using digital image processing. It begins with an overview of image enhancement, including highlighting details, removing noise, and increasing contrast. Thermal image enhancement is then discussed for applications in various fields. Key techniques covered include converting images to grayscale, histogram equalization, linear filtering for noise removal, morphology operations like erosion and dilation, and using fast Fourier transforms. A flowchart is proposed showing the sequence of applying these techniques to enhance an image.
Icamme managed brightness and contrast enhancement using adapted histogram eq...Jagan Rampalli
This document describes a new method called Controlled Contrast Modified Histogram Equalization (CCMHE) to enhance image contrast while managing brightness. CCMHE divides the input image histogram into four sub-histograms based on the median brightness value. It then applies a clipping process to prevent over-enhancement before independently equalizing each sub-histogram. CCMHE also introduces an enhancement rate parameter to control the level of contrast adjustment in the output images. The proposed method aims to produce enhanced images with improved contrast and maintained overall brightness compared to other contemporary enhancement techniques.
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Improved nonlocal means based on pre classification and invariant block matchingIAEME Publication
One of the most popular image denoising methods based on self-similarity is called nonlocal
means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings,
e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable
candidates for some non repetitive patches. In this paper, we propose to improve NLM by integrating
Gaussian blur, clustering, and row image weighted averaging into the NLM framework.
Experimental results show that the proposed technique can perform denoising better than the original
NLM both quantitatively and visually, especially when the noise level is high.
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 adaptive method for noise removal from real world imagesIAEME Publication
The document summarizes an adaptive method for noise removal from real world images. It proposes modifying the bilateral filter, which considers both spatial and intensity distances between pixels. The modified filter adapts its strength based on the local noise level in the image. It estimates the smoothing parameter by analyzing noise strength factors within blocks of different sizes. This helps determine the appropriate block size to use for a given image region. The filter aims to remove Gaussian noise while preserving edges and details to enhance image quality. Experimental results show it performs well across different images for a wide range of noise levels.
This document describes an image fusion method using pyramidal decomposition. It proposes extracting fine details from input images using guided filtering and fusing the base layers of images across multiple exposures or focal points using a multiresolution pyramid approach. A weight map is generated considering exposure, contrast, and saturation to guide the fusion of base layers. The fused base layer is then combined with extracted fine details to produce a detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions of the input images. It is argued that this method can effectively fuse images from different exposures or focal points without introducing artifacts.
www.ijera.com 68 | P a g e Leaf Disease Detection Using Arm7 and Image Proces...IJERA Editor
In an agricultural field plant diseases are very important aspect as it directly affect on the production of plant and economical value of market. In this research generally we uses image processing technique that is automatically detect symptoms of the disease as early as possible. This is the first and important phase for automatic detection and classification of plant diseases. There are some stages to find the disease like image acquisition, preprocessing on image, color transform usingYCbCr, segmentation using Otsu method, feature extraction using Gabor filter method and classification using SVM, using those steps we can surely detect the disease and classified it and also can take preventive measures.
This document discusses techniques for enhancing and analyzing thermal images using digital image processing. It begins with an overview of image enhancement, including highlighting details, removing noise, and increasing contrast. Thermal image enhancement is then discussed for applications in various fields. Key techniques covered include converting images to grayscale, histogram equalization, linear filtering for noise removal, morphology operations like erosion and dilation, and using fast Fourier transforms. A flowchart is proposed showing the sequence of applying these techniques to enhance an image.
This document summarizes a research paper that compares different image filtering methods for reducing noise, including an adaptive bilateral filter, median filter, and Butterworth filter. The paper applies these filters to images with added Gaussian white noise and compares the results based on visual quality, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). It finds that the adaptive bilateral filter produces the best results with the lowest MSE and highest PSNR, indicating it most effectively removes noise while preserving image details and sharpness.
Abstract
Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement,
classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on
which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting
the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image
enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain
enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain
enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic
and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.
Keywords: Enhancement, Spatial domain enhancement, Frequency domain enhancement, Contrast, Modality.
IMAGE DENOISING BY MEDIAN FILTER IN WAVELET DOMAINijma
The details of an image with noise may be restored by removing noise through a suitable image de-noising
method. In this research, a new method of image de-noising based on using median filter (MF) in the
wavelet domain is proposed and tested. Various types of wavelet transform filters are used in conjunction
with median filter in experimenting with the proposed approach in order to obtain better results for image
de-noising process, and, consequently to select the best suited filter. Wavelet transform working on the
frequencies of sub-bands split from an image is a powerful method for analysis of images. According to this
experimental work, the proposed method presents better results than using only wavelet transform or
median filter alone. The MSE and PSNR values are used for measuring the improvement in de-noised
images.
Developing 3D Viewing Model from 2D Stereo Pair with its Occlusion RatioCSCJournals
We intend to make a 3D model using a stereo pair of images by using a novel method of local matching in pixel domain for calculating horizontal disparities. We also find the occlusion ratio using the stereo pair followed by the use of The Edge Detection and Image SegmentatiON (EDISON) system, on one the images, which provides a complete toolbox for discontinuity preserving filtering, segmentation and edge detection. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. We then warp the segment disparities to the original image to get our final 3D viewing Model.
This document discusses image enhancement techniques in digital image processing. It defines image enhancement as modifying image attributes to make an image more suitable for a given task. The main techniques discussed are spatial domain enhancement methods like noise removal, contrast adjustment, and histogram equalization. Examples are provided to demonstrate the effects of these enhancement methods on images.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
This document provides an overview of image enhancement techniques. It discusses the objectives of image enhancement, which is to process an image to make it more suitable for a specific application or task. The document focuses on spatial domain techniques for image enhancement, specifically point processing methods and histogram processing. It categorizes image enhancement methods into two broad categories: spatial domain methods, which directly manipulate pixel values; and frequency domain methods, which first convert the image into the frequency domain before performing enhancements.
Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.
An Inclusive Analysis on Various Image Enhancement TechniquesIJMER
The document discusses various techniques for image enhancement, which is the process of improving the visual quality of digital images. It describes several techniques including filtering with morphological operators, histogram equalization, noise removal using a Wiener filter, linear contrast enhancement, median filtering, unsharp mask filtering, contrast-limited adaptive histogram equalization, and decorrelation stretch. These techniques can be used to enhance images by reducing noise, increasing contrast, sharpening edges, and highlighting subtle differences to improve interpretability. The choice of enhancement technique depends on the image content and intended application.
Zernike moment of invariants for effective image retrieval using gaussian fil...IAEME Publication
This document summarizes a research paper that proposes using Zernike moments and steerable Gaussian filters for effective image retrieval based on color, texture, and shape features. It describes extracting dominant colors from images using dynamic color quantization and clustering. Texture features are represented using steerable filter decomposition. Shape features are described using pseudo-Zernike moments. The proposed method combines these color, texture, and shape features to generate a robust feature set for image retrieval that provides better retrieval accuracy than other methods.
Image Resolution Enhancement Using Undecimated Double Density Wavelet TransformCSCJournals
This document presents a new image resolution enhancement technique using Undecimated Double Density Wavelet Transform (UDDWT). It begins with background on existing resolution enhancement methods and issues with discrete wavelet transform. It then describes the development of UDDWT and the proposed method which uses forward and inverse UDDWT to construct a high resolution image from a low resolution input image. Results show the technique improves measures like PSNR, VIF and BIQI compared to other methods, enhancing image quality. The technique offers exact shift invariance and preserves high frequency content better than interpolation methods.
Color Image Watermarking using Cycle Spinning based Sharp Frequency Localized...CSCJournals
This paper describes a new approach for color image watermarking using Cycle Spinning based Sharp Frequency Localized Contourlet Transform and Principal Component Analysis. The approach starts with decomposition of images into various subbands using Contourlet Transform(CT) successively for all the color spaces of both host and watermark images. Then principal components of middle band(x bands) are considered for inserting operation. The ordinary contourlet transform suffers from lack of frequency localization. The localization being the most important criterion for watermarking, the conventional CT is not very suitable for watermarking. This problem of CT is over come by Sharp Frequency Localized Contourlet, but this lacks of translation invariance. Hence the cycle spinning based sharp frequency localized contourlet chosen for watermarking. Embedding at middle level sub bands(x band) preserves the curve nature of edges in the host image hence less disturbance is observed when host and watermark images are compared. This result in very good Peak Signal to Noise Ratio (PSNR) instead of directly adding of mid frequency components of watermark and host images the principal components are only added. Likewise the amount of payload to be added is reduced hence host images get very less distortion. Usage of principal components also helps in fruitful extraction of watermark information from host image hence gives good correlation between input watermark and extracted one. This technique has shown a very high robustness under various intentional and non intentional attacks.
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.
The document discusses various image enhancement techniques in Matlab, including filtering, predefined filters, image enhancement tools, image restoration, dilation/erosion functions, and dithering. Filtering can be used to modify images through operations like smoothing, sharpening, and edge enhancement. Predefined filters like 'gaussian' and 'laplacian' can be applied to images with functions like fspecial and imfilter. Additional tools for operations such as histogram equalization, noise addition, and filtering are also covered.
The application of image enhancement in color and grayscale imagesNisar Ahmed Rana
This is the presentation which was presented at All Pakistan Technical Paper Competition Lahore under the title "The application of image enhancement in color and grayscale images"
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.
This document discusses image noise reduction systems. It defines two main types of images - vector images defined by control points and digital images defined as 2D arrays of pixels. It describes different types of digital images like binary, grayscale, and color images. It then discusses image noise sources, types of noise like salt and pepper, Gaussian, speckle and periodic noise. Various noise filtering techniques are presented like minimum, maximum, mean, median and rank order filtering to remove salt and pepper noise.
The document discusses various types of filters that can be used to reduce noise in digital images, including mean filters, median filters, and order statistics filters. Mean filters include arithmetic, geometric, and harmonic filters, which reduce noise by calculating the mean pixel value within a neighborhood. Median filters select the median pixel value within a neighborhood to reduce salt and pepper noise while retaining edges. Adaptive filters modify their behavior based on statistical properties of local regions in order to better reduce noise without excessive blurring.
This document summarizes a research paper that compares different image filtering methods for reducing noise, including an adaptive bilateral filter, median filter, and Butterworth filter. The paper applies these filters to images with added Gaussian white noise and compares the results based on visual quality, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). It finds that the adaptive bilateral filter produces the best results with the lowest MSE and highest PSNR, indicating it most effectively removes noise while preserving image details and sharpness.
Abstract
Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement,
classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on
which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting
the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image
enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain
enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain
enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic
and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.
Keywords: Enhancement, Spatial domain enhancement, Frequency domain enhancement, Contrast, Modality.
IMAGE DENOISING BY MEDIAN FILTER IN WAVELET DOMAINijma
The details of an image with noise may be restored by removing noise through a suitable image de-noising
method. In this research, a new method of image de-noising based on using median filter (MF) in the
wavelet domain is proposed and tested. Various types of wavelet transform filters are used in conjunction
with median filter in experimenting with the proposed approach in order to obtain better results for image
de-noising process, and, consequently to select the best suited filter. Wavelet transform working on the
frequencies of sub-bands split from an image is a powerful method for analysis of images. According to this
experimental work, the proposed method presents better results than using only wavelet transform or
median filter alone. The MSE and PSNR values are used for measuring the improvement in de-noised
images.
Developing 3D Viewing Model from 2D Stereo Pair with its Occlusion RatioCSCJournals
We intend to make a 3D model using a stereo pair of images by using a novel method of local matching in pixel domain for calculating horizontal disparities. We also find the occlusion ratio using the stereo pair followed by the use of The Edge Detection and Image SegmentatiON (EDISON) system, on one the images, which provides a complete toolbox for discontinuity preserving filtering, segmentation and edge detection. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. We then warp the segment disparities to the original image to get our final 3D viewing Model.
This document discusses image enhancement techniques in digital image processing. It defines image enhancement as modifying image attributes to make an image more suitable for a given task. The main techniques discussed are spatial domain enhancement methods like noise removal, contrast adjustment, and histogram equalization. Examples are provided to demonstrate the effects of these enhancement methods on images.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
This document provides an overview of image enhancement techniques. It discusses the objectives of image enhancement, which is to process an image to make it more suitable for a specific application or task. The document focuses on spatial domain techniques for image enhancement, specifically point processing methods and histogram processing. It categorizes image enhancement methods into two broad categories: spatial domain methods, which directly manipulate pixel values; and frequency domain methods, which first convert the image into the frequency domain before performing enhancements.
Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.
An Inclusive Analysis on Various Image Enhancement TechniquesIJMER
The document discusses various techniques for image enhancement, which is the process of improving the visual quality of digital images. It describes several techniques including filtering with morphological operators, histogram equalization, noise removal using a Wiener filter, linear contrast enhancement, median filtering, unsharp mask filtering, contrast-limited adaptive histogram equalization, and decorrelation stretch. These techniques can be used to enhance images by reducing noise, increasing contrast, sharpening edges, and highlighting subtle differences to improve interpretability. The choice of enhancement technique depends on the image content and intended application.
Zernike moment of invariants for effective image retrieval using gaussian fil...IAEME Publication
This document summarizes a research paper that proposes using Zernike moments and steerable Gaussian filters for effective image retrieval based on color, texture, and shape features. It describes extracting dominant colors from images using dynamic color quantization and clustering. Texture features are represented using steerable filter decomposition. Shape features are described using pseudo-Zernike moments. The proposed method combines these color, texture, and shape features to generate a robust feature set for image retrieval that provides better retrieval accuracy than other methods.
Image Resolution Enhancement Using Undecimated Double Density Wavelet TransformCSCJournals
This document presents a new image resolution enhancement technique using Undecimated Double Density Wavelet Transform (UDDWT). It begins with background on existing resolution enhancement methods and issues with discrete wavelet transform. It then describes the development of UDDWT and the proposed method which uses forward and inverse UDDWT to construct a high resolution image from a low resolution input image. Results show the technique improves measures like PSNR, VIF and BIQI compared to other methods, enhancing image quality. The technique offers exact shift invariance and preserves high frequency content better than interpolation methods.
Color Image Watermarking using Cycle Spinning based Sharp Frequency Localized...CSCJournals
This paper describes a new approach for color image watermarking using Cycle Spinning based Sharp Frequency Localized Contourlet Transform and Principal Component Analysis. The approach starts with decomposition of images into various subbands using Contourlet Transform(CT) successively for all the color spaces of both host and watermark images. Then principal components of middle band(x bands) are considered for inserting operation. The ordinary contourlet transform suffers from lack of frequency localization. The localization being the most important criterion for watermarking, the conventional CT is not very suitable for watermarking. This problem of CT is over come by Sharp Frequency Localized Contourlet, but this lacks of translation invariance. Hence the cycle spinning based sharp frequency localized contourlet chosen for watermarking. Embedding at middle level sub bands(x band) preserves the curve nature of edges in the host image hence less disturbance is observed when host and watermark images are compared. This result in very good Peak Signal to Noise Ratio (PSNR) instead of directly adding of mid frequency components of watermark and host images the principal components are only added. Likewise the amount of payload to be added is reduced hence host images get very less distortion. Usage of principal components also helps in fruitful extraction of watermark information from host image hence gives good correlation between input watermark and extracted one. This technique has shown a very high robustness under various intentional and non intentional attacks.
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.
The document discusses various image enhancement techniques in Matlab, including filtering, predefined filters, image enhancement tools, image restoration, dilation/erosion functions, and dithering. Filtering can be used to modify images through operations like smoothing, sharpening, and edge enhancement. Predefined filters like 'gaussian' and 'laplacian' can be applied to images with functions like fspecial and imfilter. Additional tools for operations such as histogram equalization, noise addition, and filtering are also covered.
The application of image enhancement in color and grayscale imagesNisar Ahmed Rana
This is the presentation which was presented at All Pakistan Technical Paper Competition Lahore under the title "The application of image enhancement in color and grayscale images"
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.
This document discusses image noise reduction systems. It defines two main types of images - vector images defined by control points and digital images defined as 2D arrays of pixels. It describes different types of digital images like binary, grayscale, and color images. It then discusses image noise sources, types of noise like salt and pepper, Gaussian, speckle and periodic noise. Various noise filtering techniques are presented like minimum, maximum, mean, median and rank order filtering to remove salt and pepper noise.
The document discusses various types of filters that can be used to reduce noise in digital images, including mean filters, median filters, and order statistics filters. Mean filters include arithmetic, geometric, and harmonic filters, which reduce noise by calculating the mean pixel value within a neighborhood. Median filters select the median pixel value within a neighborhood to reduce salt and pepper noise while retaining edges. Adaptive filters modify their behavior based on statistical properties of local regions in order to better reduce noise without excessive blurring.
This document discusses noise addition and filtering in images. It begins by introducing different types of digital images like binary, grayscale, and color images. It then discusses various sources of image noise like sensor heat, ISO settings, and memory failures. The main types of noise covered are salt and pepper noise, Gaussian noise, speckle noise, and uniform noise. Linear and non-linear filtering techniques are described for removing each noise type, including median filtering, Wiener filtering, and mean/Gaussian filtering. Performance of filters is evaluated using measures like mean squared error and peak signal-to-noise ratio. Matlab is mentioned for implementing noise addition and filtering.
How to Make Awesome SlideShares: Tips & TricksSlideShare
Turbocharge your online presence with SlideShare. We provide the best tips and tricks for succeeding on SlideShare. Get ideas for what to upload, tips for designing your deck and more.
SlideShare is a global platform for sharing presentations, infographics, videos and documents. It has over 18 million pieces of professional content uploaded by experts like Eric Schmidt and Guy Kawasaki. The document provides tips for setting up an account on SlideShare, uploading content, optimizing it for searchability, and sharing it on social media to build an audience and reputation as a subject matter expert.
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.
This document discusses and compares different edge detection techniques used in digital image processing, including differential operator methods (Sobel, Prewitt, Canny) and mathematical morphology. It provides details on how each technique works, such as describing the convolution kernels and steps involved. The Sobel, Roberts, Prewitt, and Canny operators detect edges by calculating the image gradient at each pixel. Morphological techniques use basic operations like erosion and dilation with structuring elements to enhance edges. The document concludes that mathematical morphology provides better edge extraction than differential methods.
Modified adaptive bilateral filter for image contrast enhancementeSAT Publishing House
This paper proposes a two-phase method for removing noise and enhancing image resolution using an adaptive bilateral filter. In the first phase, total variation regularization is used to identify pixels likely contaminated by noise. In the second phase, the image is reconstructed by applying an adaptive technique only to candidate pixels identified for enhancement. Experimental results show the proposed method performs better than median-based filters or edge-preserving regularization methods at preserving texture, details and edges even at high noise levels.
A Novel Feature Extraction Scheme for Medical X-Ray ImagesIJERA Editor
X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color
features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a
novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray
images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform
reliable matching between different views of an object or scene. GLCM represents the distributions of the
intensities and the information about relative positions of neighboring pixels of an image. The LBP features are
invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A
HOG feature vector represents local shape of an object, having edge information at plural cells. These features
have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent
experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that
good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary
patterns.
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Performance Evaluation of 2D Adaptive Bilateral Filter For Removal of Noise From Robust Images
1. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 17
Performance Evaluation of 2D Adaptive Bilateral Filter For
Removal of Noise From Robust Images
B.Sridhar srib105@gmail..com
Faculty,Department of ECE
Lendi Enggneering college
Vizianagaram535220,India
Dr.K.V.V.S.Reddy kvvsreddy@gmail.com
Professor,Department ofECE
Andhra University
Visakhapatnam, 531003, India
Abstract
In this paper, we present the performance analysis of adaptive bilateral filter by pixel to noise ratio
and mean square errors. It was evaluate changing the parameters of the adaptive filter half width
values and standard deviations. In adaptive bilateral filter, the edge slope is enhanced by
transforming the histogram via a range filter with adaptive offset and width. The variance of range
filter can also be adaptive. The filter is applied to improve the sharpens of a gray level and color
image by increasing the slope of the edges without producing overshoot or undershoots. The
related graphs were plotted and the best filter parameters are obtained
Keywords: Image Restoration, Adaptive Filter, Laplacian, Gaussian, Pixel To Noise Ratio, Mean
Square Error, Half Width Factor, Standard Deviation Factor
1. INTRODUCTION
Image restoration is the process to construct the image from a blurred and noise image. It used to
perform the operation inverse convolution methods. In basic methods the noises are to be
estimated. But in practical situations, we unable to get the information regarding blurring and
other effect directly. As specially if the robustness increase of an image it is difficult to restored.
So to improve the quality we may apply adaptive filtering methods. Bilateral filters shows the
prominent results now days, In the first step to develop a sharpening method that is
fundamentally different from the unsharp mask filter which sharpens an image by enhancing the
high frequency components of the image [4]. In the spatial domain, the boosted high-frequency
components lead to overshoot and undershoot around edges, which causes objectionable ringing
or halo artefacts. The second aspect of the problem we wish to address is noise[14]. In terms of
noise removal, conventional linear filters work well for removing additive Gaussian noise, but they
also significantly blur the edge structures of an image. Therefore, a great deal of research has
been done on edge-preserving noise reduction[5,6]. The bilateral filter is essentially a smoothing
filter; it does not restore the sharpness of a degraded image. The idea of bilateral filtering has
since found its way into many applications not only in the area of image de-noising, but also
computer graphics, video processing, image interpolation, dynamic range compression, and
several others.
This paper examines the performance of the bilateral filtering, a recent approach proposed in [3]
that represents the optimum parameter to chosen to get restored image form the robust image.
The quality parameters are PSNR and MSE. Applications of bilateral filtering is varied (see, for
example, the mean shift filtering [2] applications, mean shift and bilateral filtering, are closely
related the repaid advancements of computing technology, any use of the computer-based
technologies.
2. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 18
2. INTRODUCTION TO BILATERAL FILTER
The Bilateral filtering was proposed by Tomasi and Manduchi in 1998 as a non-iterative method
for edge-preserving, smoothing and noise reducing smoothing filter[3]. The intensity value at each
pixel in an image is replaced by a weighted average of intensity values from nearby pixels. This
weight is based on a Gaussian distribution. Crucially the weights depend not only on Euclidean
distance but also on the radiometric differences (differences in the range, e.g. color intensity).
This preserves sharp edges by systematically looping through each pixel and according weights
to the adjacent pixels accordingly.
performs 2-D bilateral filtering for the grayscale or color image A. A should be a double precision
matrix of size NxMx1 or NxMx3 (i.e., grayscale or color images, respectively) with normalized
values in the closed interval [0, 1].
as shown in the fig.1 the response of bilateral filter. The intensity value at each pixel in an image
is replaced by a weighted average of intensity values from nearby pixels. This weight is based on
a Gaussian distribution. Crucially the weights depend not only on Euclidean distance but also on
the radiometric differences (differences in the range, e.g. color intensity). This preserves sharp
edges by systematically looping through each pixel and according weights to the adjacent pixels
accordingly
FIGURE 1: Response of Bilateral Filtering.
3. ADAPTIVE BILATERAL FILTERS
In adaptive filtering process here is using a combination of filtering process laplacian and
Gaussian to improve the sharpness of edges of the image. the functional and graphical
representation is shown in the figure 2,3.
The Laplacian is a 2-D isotropic measure of the 2nd spatial derivative of an image. The Laplacian
of an image highlights regions of rapid intensity change and is therefore often used for edge
detection (see zero crossing edge detectors). The Laplacian is often applied to an image that has
first been smoothed with something approximating a Gaussian smoothing filter in order to reduce
its sensitivity to noise, and hence the two variants will be described together here. The operator
normally takes a single graylevel image as input and produces another graylevel image as
output.[3].In fact, since the convolution operation is associative, we can convolve the Gaussian
smoothing filter with the Laplacian filter first of all, and then convolve this hybrid filter with the
image to achieve the required result. Doing things this way has two advantages:[7,8]
• Since both the Gaussian and the Laplacian kernels are usually much smaller than the
image, this method usually requires far fewer arithmetic operations.
• The LoG (`Laplacian of Gaussian') kernel can be precalculated in advance one
convolution needs to be performed at run-time on the image. The 2-D LoG function
3. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 19
centered on zero and with Gaussian standard deviation has the form:
as shown in the figure 2
FIGURE 2: The 2-D Laplacian of Gaussian (LoG) Function.
The x and y axes are marked in standard deviations ( ).
A discrete kernel that approximates this function (for a Gaussian = 1.4) is shown in Fig.
FIGURE 3: Discrete Approximation To LoG Function With Gaussian = 1.
Note that as the Gaussian is made increasingly narrow, the LoG kernel becomes the same as the
simple Laplacian kernels shown in Figure 3. This is because smoothing with a very narrow
Gaussian ( < 0.5 pixels) on a discrete grid has no effect. Hence on a discrete grid, the simple
Laplacian can be seen as a limiting case of the LoG for narrow Gaussians.[12,13]
4. EXPERIMENTAL DETAILS
In this section we analyze the performance of bilateral filters on gray level and color images. The
proposed algorithm shows in the figure 4. The two most common forms of degradation an image
suffers are loss of sharpness or blur and noise. The degradation model consists of a linear shift-
invariant blur followed by additive noise . in this paper a common process can choose at time
0 1 1 2 2 2 1 1 0
1 2 4 5 5 5 4 2 1
1 4 5 3 0 3 5 4 1
2 5 3 -
12
-
24
-
12
3 5 2
2 5 0 -
12
-
40
-
24
0 5 2
2 5 3 -
12
-
24
-
12
3 5 2
1 4 5 3 0 3 5 4 1
1 2 4 5 5 5 4 2 1
0 1 1 2 2 2 1 1 0
4. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 20
both gray and color images. It is simulated in MATLAB, the results and explanations as explained
in the section –5.
Gray color
FIGURE 4: Flowchart For Proposed Method.
4.1 Mean Square Error
MSE is a risk function, corresponding to the expected value of the squared error loss or quadratic
loss. MSE measures the average of the squares of the "errors." The error is the amount by which
the value implied by the estimator differs from the quantity to be estimated. The difference occurs
because of randomness or because the estimator doesn't account for information that could
produce a more accurate estimate.[9].
The MSE is the second moment (about the origin) of the error, and thus incorporates both
the variance of the estimator and its bias. For an unbiased estimator, the MSE is the variance.
Like the variance, MSE has the same units of measurement as the square of the quantity being
estimated.
The MSE of an estimator with respect to the estimated parameter is defined as
The MSE is equal to the sum of the variance and the squared bias of the estimator
Load the images
Calucalte the LOG response
Obtain the parameters of filter
Apply the histogram
Is image
is gray/
color
Calucalte the LOG response
Calucalte the new filter weights Calucalte the new color filter weights
Display the generated output
gray image
Display the generated output color
image
Caluclate PSNR&MSE for input
and out image
Display the calculated values
5. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 21
Thus assesses the quality of an estimator in terms of its variation and unbiasedness. Tthe MSE is
not equivalent to the expected value of the absolute error. Since MSE is an expectation, it is not a
random variable. It may be a function of the unknown parameter , but it does not depend on any
random quantities. However, when MSE is computed for a particular estimator of the true value
of which is not known, it will be subject to estimation error. In a Bayesian sense, there are cases
in which it may be treated as a random variable.
4.2 Peak Signal to Noise Ratio
The peak signal-to-noise ratio (PSNR) is the ratio between a signal's maximum power and the
power of the signal's noise. Signals can have a wide dynamic range, so PSNR is usually
expressed in decibels, which is a logarithmic scale[10,11]. It is most easily defined via the mean
squared error (MSE) which for two m×n monochrome images I and K where one of the images is
considered a noisy approximation of the other is defined as:
The PSNR is defined as:
Here, MAXI is the maximum possible pixel value of the image. Typical values for the PSNR
in lossy image and video compression are between 30 and 50 dB, where higher is better.
Acceptable values for wireless transmission quality loss are considered to be about 20 dB to
25 dB.
5. RESULTS DISCUSSIONS
In this chapter we will discuss the results of gray and color images plotted for different values of
bilateral filter width and MSEs and PSNRs and taken the best values of half width(w) and
standard deviation.
5.1 Input Figures
The figures 5 and 6 are taken as the input images for the implementation of Adaptive Bilateral
Filter i.e., these images are loaded into MATLAB environment. The adaptive bilateral filtering is
applied to both gray scale image and color image.
6. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 22
FIGURE 5: Input Gray Image. FIGURE 6: Input Color Image.
The dimensions of the grayscale image are 256x256 and the dimension of the color image is
256x256x3.
5.2 Histogram
An image histogram is a graphical representation of the tonal distribution in a digital image. It
plots the number of pixels for each tonal value. The histogram for a specific image allows a
viewer to judge the entire tonal distribution at a glance. The function “hist” displays the histogram
of the images in Cartesian coordinate system.
FIGURE 7: Histogram Function of Gray Image.
The Fig: 7 shows the histogram of the input gray image which represents the intensity distribution
of different pixels in the image.
FIGURE 8: Histogram Function of Color Image.
7. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 23
The Figure 8 shows the histogram of the input colour image which represents the intensity
distribution of different pixels in the image.
5.3 Output of Adaptive Bilateral Filter
After setting the bilateral parameters like bilateral filter width, domain and range filter standard
deviations adaptive bilateral filtering is applied to both the images using the function
FIGURE 9: Adaptive Bilateral Filter Result for Gray Image.
The figure 9 shows the input gray image which is affected by some kind of noise and the output
image after adaptive bilateral filtering. The input gray image appears blurred with unsharpened
edges. As adaptive bilateral filter is the most effective one in removing the noise it enhances the
slope without generating overshoot and undershoot around the edges. The output image is much
clear compared to the input image because of the noise removal and edge preserving.
FIGURE 10: Adaptive Bilateral Filter Result for Color Image.
The figure 10 shows the input color image which is affected by some kind of noise and the output
image after adaptive bilateral filtering. The input color image appears blurred with unsharpened
edges. As adaptive bilateral filter is the most effective one in removing the noise it enhances the
slope without generating overshoot and undershoot around the edges. The output image is much
clear compared to the input image because of the noise removal and edge preserving.
8. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 24
FIGURE 11: Histogram Function of Gray Image After Filtering.
Figure: 12: Histogram Function of Color Image After Filtering.
Figures 11 and 12 represents the intensity levels of gray image and color image respectively after
applying the adaptive bilateral filter.
5.5 Calculation of MSE And PSNR
By practically varying different values of the bilateral half width and by taking range filter standard
deviation as σR = 0.1 the mean squared errors for gray image is measured and are tabulated as
follows:
9. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 25
TABLE 1: MSE And PSNR Values For Gray And Color Images For Fixed Values of σR.
The above Table .1 shows the vlaues for peak signal to noise ratio and mean squarred error for
both gray and color images taken at fixed values of standard range deviation (σR) against varying
the values of half width (W)
FIGURE 13: MSE Plot For Gray Image For Different Values of σR.
The above figure 13 shows the graph of a gray image plotted between MSE on x-axis and
bilateral filter width on y-axis. For different values of bilateral filter standard range deviation σR,
the values are taken between 0.1 to 0.4 and the respective graphs are being plotted.
w σR Mean Squared Error Peak signal to
noise ratio
For Gray
image
For Color
image
For
Gray
image
For
Color
image
2 0.1 0.000501083 0.0016723 32.9944 27.7668
3 0.1 0.00050981 0.0019067 32.9259 27.1973
4 0.1 0.00050983 0.0020941 32.9257 26.7901
5 0.1 0.0005276 0.002253 32.7781 26.4724
2 0.2 0.00049998 0.0016711 33.0105 27.7701
3 0.2 0.00050168 0.0018949 32.9957 27.2241
4 0.2 0.00051173 0.002093 32.9096 26.7922
5 0.2 0.00052602 0.0022611 32.79 26.4569
2 0.3 0.00049903 0.0016618 33.0188 27.7942
3 0.3 0.00050871 0.0018971 32.9353 27.2193
4 0.3 0.00051518 0.0016618 32.8804 26.7983
5 0.3 0.00052855 0.0020901 32.7691 26.4465
2 0.4 0.00049568 0.001602 33.048 27.7828
3 0.4 0.00050479 0.0019082 32.9689 27.1938
4 0.4 0.00050963 0.0020929 32.9275 26.7925
5 0.4 0.00053133 0.0022544 32.7463 26.4697
10. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 26
FIGURE 14: PSNR Plot for Gray Image For Different Values of σR.
The above figure 14 shows the graph of a gray image plotted between PSNR on y-axis and
bilateral filter width on x-axis. For different values of bilateral filter standard range deviation σR,
the values are taken between 0.1 to 0.4 and the respective graphs are being plotted.
FIGURE 15: MSE Plot for Color Image For Different Values of σR.
The above figure 15 shows the graph of a color image plotted between MSE on x-axis and
bilateral filter width on y-axis. For different values of bilateral filter standard range deviation σR,
the values are taken between 0.1 to 0.4 and the respective graphs are being plotted.
FIGURE 16: PSNR Plot for Color Image For Different Values of σR.
11. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 27
The above figure 16 shows the graph of a color image plotted between PSNR on y-axis and
bilateral filter width on x-axis. For different values of bilateral filter standard range deviation σR,
the values are taken between 0.1 to 0.4 and the respective graphs are being plotted.
w σR Mean Squared Error Peak signal to
noise ratio
For Gray
image
For Color
image
For
Gray
image
For
Color
image
2 0.1 0.00049729 0.0016573 33.0339 27.806
2 0.2 0.00050549 0.0016601 32.9629 27.7985
2 0.3 0.00049736 0.0016753 33.0333 27.759
2 0.4 0.00051037 0.0016589 32.9211 27.8019
3 0.1 0.00050324 0.0018911 33.001 27.2089
3 0.2 0.00050741 0.0019097 32.9464 27.1903
3 0.3 0.00050201 0.0018941 32.9929 27.226
3 0.4 0.00050843 0.0019041 32.9372 27.2036
4 0.1 0.00050887 0.0021106 32.934 26.756
4 0.2 0.00051846 0.002081 32.859 26.8173
4 0.3 0.00051271 0.002081 32.9013 26.8173
4 0.4 0.00050983 0.0020941 32.9257 26.7901
5 0.1 0.00053307 0.0022733 32.7322 26.4335
5 0.2 0.00052872 0.0022879 32.7677 24.4056
5 0.3 0.00052521 0.0022728 32.7967 26.4343
5 0.4 0.00052528 0.0022591 32.7961 26.4607
TALBE 2: MSE and PSNR Values For Gray And Color Images For Fixed Values of W.
The above table 5.2 shows the vlaues for peak signal to noise ratio and mean squarred error for
both gray and color images taken at fixed values of half width (W) against varying the values of
standard range deviation(σR)
FIGURE 17: MSE Plot for Gray Image For Different Values of w.
The above figure 17 shows the graph of a gray image plotted between MSE on x-axis and
bilateral filter standard range deviation σR on y-axis. For different values of bilateral filter half
width(W), the values are taken between 2 to 5 and the respective graphs are being plotted.
12. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 28
FIGURE 18: PSNR Plot for Gray Image For Different Values of w.
The above figure: 18 shows the graph of a gray image plotted between PSNR on x-axis and
bilateral filter standard range deviation σR on y-axis. For different values of bilateral filter half
width(W), the values are taken between 2 to 5 and the respective graphs are being plotted.
FIGURE 19: MSE Plot for Color Image For Different Values of w.
The above figure: 19. shows the graph of a color image plotted between MSE on x-axis and
bilateral filter standard range deviation σR on y-axis. For different values of bilateral filter half
width(W), the values are taken between 2 to 5 and the respective graphs are being plotted.
13. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 29
FIGURE 20: PSNR Plot for Color Image For Different Values of w.
The above figure: 20 shows the graph of a color image plotted between PSNR on x-axis and
bilateral filter standard range deviation σR on y-axis. For different values of bilateral filter half
width(W), the values are taken between 2 to 5 and the respective graphs are being plotted.
The proposed method was applied to gray level and color with different standard deviation
noises. The PSNR of the restored image was measured for the proposed method as well as the
original bilateral filtering method and the state-of-the-art image denoising method using Gaussian
scale mixtures proposed by wang. [6] for comparison. A summary of the results is states that the
average of PSNR is around 31. The proposed method achieves noticeable PSNR gains over 33
the original bilateral filtering method for all of the gray level. Also the color image signal filtering
shows prominent results. It can be observed that the proposed method produced a restored
image signal with noticeably improved perceptual quality compared to the Wang and the original
bilateral filtering method.
6. CONCLUSIONS
In this paper, we present the adaptive bilateral filter for sharpness enhancement and noise
removal. The adaptive bilateral filter sharpens an image by increasing the slope of the edges
without producing overshoot or undershoots. In the adaptive bilateral filter, the edge slope is
enhanced by transforming the histogram via a range filter with adaptive offset and width. The
variance of range filter can also be adaptive. The adaptive bilateral filter is able to smooth the
noise, while enhancing edges and textures in the image. Adaptive bilateral filter restored images
are to be significantly sharper than those restored by the bilateral filter. The mean square error
and peak signal to noise ratios are also calculated for different values of bilateral filter width and
range filter standard deviation and corresponding graphs are plotted for both gray and color
images. Adaptive bilateral filter works well for both gray images and color images. For future
development of the adaptive bilateral filter, we would suggest that the following issues be
addressed. First, the adaptive bilateral filter tends to resize the image, due to its fundamental
mechanism of sharpening an image by pulling up or pushing down pixels along the edge slope.
Second, the adaptive bilateral filter does not perform as well at corners as it does on lines and
spatially slow-varying curves, since the adaptive bilateral filter is primarily based on transforming
the histogram of the local data, which cannot effectively represent 2-D structures. Finally, in the
current design of the adaptive bilateral filter, a fixed domain Gaussian filter is used. Future
developments this proposed method does not work efficiently at corner edges, by choose a
addition of transformation operation along with proposed method the problem will be solved.
14. B.Sridhar & Dr.K.V.V.S.Reddy
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 30
7. REFERENCES
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[9] http://www4.comp.polyu.edu.hk/~cslzhang/code.htm
[10] http://www4.comp.polyu.edu.hk/~cslzhang/
[11] http://sse.tongji.edu.cn/linzhang/
[12] http://sse.tongji.edu.cn/linzhang/UsefulLinks/links.htm
[13] http://www.csse.uwa.edu.au/~ajmal/code.html
[14]http://vlm1.uta.edu/~athitsos/vlm/
[15]http://users.soe.ucsc.edu/~milanfar/students/
[16].susan approach-----http://users.fmrib.ox.ac.uk/~steve/susan/index.html