A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this
paper. This technique modifies the probability density function of an image by introducing constraints prior
to the process of histogram equalization (HE). These constraints are formulated using two parameters
which are optimized using swarm intelligence. This technique of contrast enhancement takes control over
the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment
factor is then added to the result to normalize the change in the luminance level after enhancement. This
factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of
highly deviated intensities have greater impact in changing the contrast of an image. This approach
provides a convenient and effective way to control the enhancement process, while being adaptive to
various types of images. Experimental results show that the proposed technique gives better results in
terms of Discrete Entropy and SSIM values than the existing histogram-based equalization methods.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various HE methods have been proposed. These methods preserves the brightness on the output image but, does not have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which decompose the image into several sub images, and classical HE method is applied to each sub image. The algorithm is applied on various images and has been analysed using both objective and subjective assessment.
Review on Image Enhancement in Spatial Domainidescitation
With the proliferation in electronic imaging devices
like in mobiles, computer vision, medical field and space field;
image enhancement field has become the quite interesting
and important area of research. These imaging devices are
viewed under a diverse range of viewing conditions and a huge
loss in contrast under bright outdoor viewing conditions; thus
viewing condition parameters such as surround effects,
correlated color temperature and ambient lighting have
become of significant importance. Therefore, Principle
objective of Image enhancement is to adjust the quality of an
image for better human visual perception. Appropriate choice
of enhancement techniques is greatly influenced by the
imaging modality, task at hand and viewing conditions.
Basically, image enhancement techniques have been classified
into two broad categories: Spatial domain image enhancement
and Frequency domain image enhancement. This survey report
gives an overview of different methodologies have been used
for enhancement under the spatial domain category. It is noted
that in this field still more research is to be done.
A comprehensive method for image contrast enhancement based on global –local ...eSAT Publishing House
This document presents a method for image contrast enhancement based on combining global and local contrast techniques. It addresses issues with existing local standard deviation based methods when applied to constant image areas, which results in divided by zero errors. The proposed method modifies the local standard deviation calculation by adding a small value to avoid this, allowing contrast enhancement to be applied across the entire image without information loss. Experimental results on test images demonstrate improved contrast enhancement over existing methods, as measured by peak signal-to-noise ratio values.
CONTRAST ENHANCEMENT AND BRIGHTNESS PRESERVATION USING MULTIDECOMPOSITION HIS...sipij
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world.
Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization
(AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE)
methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final
image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization
(MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input
image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in
correct order. The final image after MDHE gives us the best results based on contrast enhancement and
brightness preservation aspect compared to all other techniques mentioned above. We have calculated the
various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
A comparative study of histogram equalization based image enhancement techniq...sipij
Histogram Equalization is a contrast enhancement te
chnique in the image processing which uses the
histogram of image. However histogram equalization
is not the best method for contrast enhancement
because the mean brightness of the output image is
significantly different from the input image. There
are
several extensions of histogram equalization has be
en proposed to overcome the brightness preservation
challenge. Contrast enhancement using brightness pr
eserving bi-histogram equalization (BBHE) and
Dualistic sub image histogram equalization (DSIHE)
which divides the image histogram into two parts
based on the input mean and median respectively the
n equalizes each sub histogram independently. This
paper provides review of different popular histogra
m equalization techniques and experimental study ba
sed
on the absolute mean brightness error (AMBE), peak
signal to noise ratio (PSNR), Structure similarity
index
(SSI) and Entropy.
HUMAN VISION THRESHOLDING WITH ENHANCEMENT FOR DARK BLURRED IMAGES FOR LOCAL ...cscpconf
There are several images that do not have uniform brightness which pose a challenging problem
for image enhancement systems. As histogram equalization has been successfully used to correct
for uniform brightness problems, a histogram equalization method that utilizes human visual
system based thresholding(human vision thresholding) as well as logarithmic processing
techniques were introduced later . But these methods are not good for preserving the local
content of the image which is a major factor for various images like medical images.Therefore
new method is proposed here. This method is referred as “Human vision thresholding with
enhancement technique for dark blurred images for local content preservation”. It uses human
vision thresholding together with an existing enhancement method for dark blurred images.
Experimental results shows that the proposed method outperforms the former existing methods in
preserving the local content for standard images and medical images
Contrast enhancement using various statistical operations and neighborhood pr...sipij
This document proposes a novel contrast enhancement algorithm using various statistical operations and neighborhood processing. It begins with an overview of histogram equalization and some of its limitations. It then discusses related work on other histogram equalization techniques including classical histogram equalization, brightness preserving bi-histogram equalization, recursive mean separate histogram equalization, and background brightness preserving histogram equalization. The proposed method is then described, which applies statistical operations like mean and standard deviation within a neighborhood to locally enhance pixels. Pixels are replaced from an initially equalized image if their difference from the local mean exceeds a threshold. This aims to preserve local brightness features. Finally, metrics for evaluating image quality like PSNR, SSIM, and CNR are defined to analyze results
This document reviews various histogram equalization techniques for image enhancement. It begins by introducing histograms and histogram equalization. It then describes 10 histogram equalization techniques in detail: classical histogram equalization, adaptive histogram equalization, bi-histogram equalization, brightness preserving bi-histogram equalization, minimum mean brightness error bi-histogram equalization, dualistic sub-image histogram equalization, recursive mean separate histogram equalization, multi-decomposition histogram equalization, dynamic histogram equalization, and brightness preserving dynamic histogram equalization. It concludes by discussing applications of these techniques in fields like satellite communications, medical imaging, and surveillance.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various HE methods have been proposed. These methods preserves the brightness on the output image but, does not have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which decompose the image into several sub images, and classical HE method is applied to each sub image. The algorithm is applied on various images and has been analysed using both objective and subjective assessment.
Review on Image Enhancement in Spatial Domainidescitation
With the proliferation in electronic imaging devices
like in mobiles, computer vision, medical field and space field;
image enhancement field has become the quite interesting
and important area of research. These imaging devices are
viewed under a diverse range of viewing conditions and a huge
loss in contrast under bright outdoor viewing conditions; thus
viewing condition parameters such as surround effects,
correlated color temperature and ambient lighting have
become of significant importance. Therefore, Principle
objective of Image enhancement is to adjust the quality of an
image for better human visual perception. Appropriate choice
of enhancement techniques is greatly influenced by the
imaging modality, task at hand and viewing conditions.
Basically, image enhancement techniques have been classified
into two broad categories: Spatial domain image enhancement
and Frequency domain image enhancement. This survey report
gives an overview of different methodologies have been used
for enhancement under the spatial domain category. It is noted
that in this field still more research is to be done.
A comprehensive method for image contrast enhancement based on global –local ...eSAT Publishing House
This document presents a method for image contrast enhancement based on combining global and local contrast techniques. It addresses issues with existing local standard deviation based methods when applied to constant image areas, which results in divided by zero errors. The proposed method modifies the local standard deviation calculation by adding a small value to avoid this, allowing contrast enhancement to be applied across the entire image without information loss. Experimental results on test images demonstrate improved contrast enhancement over existing methods, as measured by peak signal-to-noise ratio values.
CONTRAST ENHANCEMENT AND BRIGHTNESS PRESERVATION USING MULTIDECOMPOSITION HIS...sipij
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world.
Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization
(AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE)
methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final
image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization
(MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input
image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in
correct order. The final image after MDHE gives us the best results based on contrast enhancement and
brightness preservation aspect compared to all other techniques mentioned above. We have calculated the
various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
A comparative study of histogram equalization based image enhancement techniq...sipij
Histogram Equalization is a contrast enhancement te
chnique in the image processing which uses the
histogram of image. However histogram equalization
is not the best method for contrast enhancement
because the mean brightness of the output image is
significantly different from the input image. There
are
several extensions of histogram equalization has be
en proposed to overcome the brightness preservation
challenge. Contrast enhancement using brightness pr
eserving bi-histogram equalization (BBHE) and
Dualistic sub image histogram equalization (DSIHE)
which divides the image histogram into two parts
based on the input mean and median respectively the
n equalizes each sub histogram independently. This
paper provides review of different popular histogra
m equalization techniques and experimental study ba
sed
on the absolute mean brightness error (AMBE), peak
signal to noise ratio (PSNR), Structure similarity
index
(SSI) and Entropy.
HUMAN VISION THRESHOLDING WITH ENHANCEMENT FOR DARK BLURRED IMAGES FOR LOCAL ...cscpconf
There are several images that do not have uniform brightness which pose a challenging problem
for image enhancement systems. As histogram equalization has been successfully used to correct
for uniform brightness problems, a histogram equalization method that utilizes human visual
system based thresholding(human vision thresholding) as well as logarithmic processing
techniques were introduced later . But these methods are not good for preserving the local
content of the image which is a major factor for various images like medical images.Therefore
new method is proposed here. This method is referred as “Human vision thresholding with
enhancement technique for dark blurred images for local content preservation”. It uses human
vision thresholding together with an existing enhancement method for dark blurred images.
Experimental results shows that the proposed method outperforms the former existing methods in
preserving the local content for standard images and medical images
Contrast enhancement using various statistical operations and neighborhood pr...sipij
This document proposes a novel contrast enhancement algorithm using various statistical operations and neighborhood processing. It begins with an overview of histogram equalization and some of its limitations. It then discusses related work on other histogram equalization techniques including classical histogram equalization, brightness preserving bi-histogram equalization, recursive mean separate histogram equalization, and background brightness preserving histogram equalization. The proposed method is then described, which applies statistical operations like mean and standard deviation within a neighborhood to locally enhance pixels. Pixels are replaced from an initially equalized image if their difference from the local mean exceeds a threshold. This aims to preserve local brightness features. Finally, metrics for evaluating image quality like PSNR, SSIM, and CNR are defined to analyze results
This document reviews various histogram equalization techniques for image enhancement. It begins by introducing histograms and histogram equalization. It then describes 10 histogram equalization techniques in detail: classical histogram equalization, adaptive histogram equalization, bi-histogram equalization, brightness preserving bi-histogram equalization, minimum mean brightness error bi-histogram equalization, dualistic sub-image histogram equalization, recursive mean separate histogram equalization, multi-decomposition histogram equalization, dynamic histogram equalization, and brightness preserving dynamic histogram equalization. It concludes by discussing applications of these techniques in fields like satellite communications, medical imaging, and surveillance.
Image enhancement plays an important role in vision applications. Recently a lot of work has been performed in the field of image enhancement. Many techniques have already been proposed till now for enhancing the digital images. This paper has presented a comparative analysis of various image enhancement techniques. This paper has shown that the fuzzy logic and histogram based techniques have quite effective results over the available techniques. This paper ends up with suitable future directions to enhance fuzzy based image enhancement technique further. In the proposed technique, an approach is made to enhance the images other than low-contrast images as well by balancing the stretching parameter (K) according to the color contrast. Proposed technique is designed to restore the degraded edges resulted due to contrast enhancement as well.
Image enhancement technique plays vital role in improving the quality of the image. Enhancement
technique basically enhances the foreground information and retains the background and improve the
overall contrast of an image. In some case the background of an image hides the structural information of
an image. This paper proposes an algorithm which enhances the foreground image and the background
part separately and stretch the contrast of an image at inter-object level and intra-object level and then
combines it to an enhanced image. The results are compared with various classical methods using image
quality measures
Contrast Enhancement Techniques: A Brief and Concise ReviewIRJET Journal
The document discusses various contrast enhancement techniques for digital images. It provides an overview of techniques such as histogram equalization, which works by flattening the histogram and stretching the dynamic range of gray levels. Global histogram equalization uses the entire image histogram for contrast adjustment, while local histogram equalization processes the image in blocks to better preserve local details and brightness levels. The techniques aim to improve image quality by increasing luminance differences between foreground and background.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Content adaptive single image interpolation based Super Resolution of compres...IJECEIAES
Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling. This paper focuses on upscaling of compressed images with interpolation based Single Image Super Resolution technique. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super Resolution methods. The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on a single image and uses the content of the original image only; therefore, the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method.
7 ijaems sept-2015-8-design and implementation of fuzzy logic based image fus...INFOGAIN PUBLICATION
The quality of image holds importance for both humans and machines. To fulfill the requirement of good quality images, image enhancement is needed. Application of a single contrast enhancement technique often does not produce desirable result and may lead to over enhanced images. To overcome this problem image fusion is performed so that better results with desired enhancement can be achieved. In the present paper an amalgamation of image enhancement, fusion and sharpening have been carried out in the candidate algorithm. The algorithm makes use of fuzzy logic for weight calculation. The results are compared with DACE/LIF approach and it is observed that the proposed algorithm improves the result in terms of quality parameters like PSNR (Peak Signal to Noise Ratio), AMBE (Absolute Mean Brightness Error) and SSIM (Structural Similarity Index) by 0.5 dB, 3 and 0.1 respectively from the existing technique.
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.
Analysis of collaborative learning methods for image contrast enhancementIAEME Publication
The document describes collaborative learning methods for image contrast enhancement. It begins with background on image enhancement techniques like histogram equalization. It then summarizes an existing collaborative learning method that determines pixel values from multiple randomly sampled windows. The document proposes a modified method that combines collaborative learning with block-based histogram equalization using randomly sized sliding windows. It is evaluated on medical and underwater images and is found to provide better results than the original collaborative learning method. Quality metrics are used to measure enhancement.
Quality Assessment of Gray and Color Images through Image Fusion TechniqueIJEEE
. Image fusion is an emerging trend in the digital image processing to enhance images. In image fusion two or more images can be fused (combined) to obtain an enhanced image. In the present work image fusion technology has been used to enhance a given input image. Image fusion is used here to combine two images which contains complementary information.
Performance Evaluation of Filters for Enhancement of Images in Different Appl...IOSR Journals
This document evaluates the performance of different filters for enhancing images in various application areas. It discusses contrast stretching and histogram equalization as common spatial domain techniques for contrast enhancement. Bi-histogram equalization was introduced to preserve image brightness during contrast enhancement. However, contrast enhancement also enhances noise, causing blurriness. The proposed BHEGF method aims to reduce this while providing more accurate results. The document uses median, Gaussian, average, and motion filters and evaluates them based on processing time, mean squared error, brightness count, and peak signal-to-noise ratio to determine which filter provides the best performance for image enhancement.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
A MODIFIED HISTOGRAM BASED FAST ENHANCEMENT ALGORITHMcsandit
The contrast enhancement of medical images has an important role in diseases diagnostic,
specially, cancer cases. Histogram equalization is considered as the most popular algorithm for
contrast enhancement according to its effectiveness and simplicity. In this paper, we present a
modified version of the Histogram Based Fast Enhancement Algorithm. This algorithm
enhances the areas of interest with less complexity. It is applied only to CT head images and its
idea based on treating with the soft tissues and ignoring other details in the image. The
proposed modification make the algorithm is valid for most CT image types with enhanced
results.
Modified Contrast Enhancement using Laplacian and Gaussians Fusion Techniqueiosrjce
The aim of image fusion is to mix images of a scene captured below totally different illumination. One
image contains most of information from the whole supply images automatically. Contrast enhancement is employed
to enhance the standard of visible image with none introducing unrealistic visual appearances. Fusion technique is
employed for the important applications like medical imaging, microscopic imaging, remote sensing, and laptop
vision and robotics. Contrast enhancement improves the brightness differences within the dark, gray or bright regions
at the expense of the brightness differences within the alternative regions. During this paper methodology of the
contrast enhancement for images that improves the local image contrast by controlling the local image gradient. The
proposed methodology improves the improvement drawback and maximizes the local contrast and global contrast of
an image.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
In this project we have implemented a tool to inpaint selected regions from an image. Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information. The tool provides a user interface wherein the user can open an image for inpainting, select the parts
of the image that he wants to reconstruct. The tool would then automatically inpaint the selected area according to the background information. The image can
then be saved. The inpainting in based on the exemplar based approach. The basic aim of this approach is to find examples (i.e. patches) from the image and
replace the lost data with it. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like
dates, subtitles etc.; and the removal of entire objects from the image like microphones or wires in special effects.
Image in Painting Techniques: A survey IOSR Journals
This document provides a survey of different image inpainting techniques. It discusses approaches such as texture synthesis based inpainting, PDE (partial differential equation) based inpainting, exemplar based inpainting, hybrid inpainting, and semi-automatic inpainting. Texture synthesis approaches recreate textures within missing regions by sampling from surrounding textures. PDE based methods diffuse image information into missing areas. Exemplar based techniques iteratively copy patches from surrounding regions. Hybrid methods combine approaches. The document analyzes strengths and limitations of each technique.
The document reviews approaches to image interpolation and super-resolution. It discusses several interpolation methods including polynomial-based, edge-directed, and soft-decision approaches. Edge-directed methods aim to preserve edge sharpness during upsampling by estimating edge orientations or fusing multiple orientations. New edge-directed interpolation uses a Wiener filter to estimate missing pixel values. Soft-decision adaptive interpolation and robust soft-decision interpolation further improve results by modeling image signals within local windows and incorporating outlier weighting. The document provides formulations and comparisons of these methods.
This document summarizes research on image enhancement techniques in the spatial domain. It begins by introducing two categories of image enhancement - spatial domain and frequency domain methods. It then reviews various histogram equalization techniques for contrast enhancement in grayscale images, including global, local, and block-based approaches. The document also discusses extensions of histogram equalization as well as techniques that consider color images and use fuzzy logic approaches. It concludes that histogram equalization is commonly used for contrast enhancement but has limitations, and fuzzy logic methods show promise for color image enhancement.
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A study of a modified histogram based fast enhancement algorithm (mhbfe)sipij
Image enhancement is one of the most important issues in low-level image processing. The goal of image
enhancement is to improve the quality of an image such that enhanced image is better than the original
image. Conventional Histogram equalization (HE) is one of the most algorithms used in the contrast
enhancement of medical images, this due to its simplicity and effectiveness. However, it causes the
unnatural look and visual artefacts, where it tends to change the brightness of an images. The Histogram
Based Fast Enhancement Algorithm (HBFE) tries to enhance the CT head images, where it improves the
water-washed effect caused by conventional histogram equalization algorithms with less complexity. It
depends on using full gray levels to enhance the soft tissues ignoring other image details. We present a
modification of this algorithm to be valid for most CT image types with keeping the degree of simplicity.
Experimental results show that The Modified Histogram Based Fast Enhancement Algorithm (MHBFE)
enhances the results in term of PSNR, AMBE and entropy. We use also the Statistical analysis to ensure
the improvement of the proposed modification that can be generalized. ANalysis Of VAriance (ANOVA) is
used as first to test whether or not all the results have the same average. Then we find the significant
improvement of the modification.
Enhancement of Medical Images using Histogram Based Hybrid TechniqueINFOGAIN PUBLICATION
Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform.
Image enhancement plays an important role in vision applications. Recently a lot of work has been performed in the field of image enhancement. Many techniques have already been proposed till now for enhancing the digital images. This paper has presented a comparative analysis of various image enhancement techniques. This paper has shown that the fuzzy logic and histogram based techniques have quite effective results over the available techniques. This paper ends up with suitable future directions to enhance fuzzy based image enhancement technique further. In the proposed technique, an approach is made to enhance the images other than low-contrast images as well by balancing the stretching parameter (K) according to the color contrast. Proposed technique is designed to restore the degraded edges resulted due to contrast enhancement as well.
Image enhancement technique plays vital role in improving the quality of the image. Enhancement
technique basically enhances the foreground information and retains the background and improve the
overall contrast of an image. In some case the background of an image hides the structural information of
an image. This paper proposes an algorithm which enhances the foreground image and the background
part separately and stretch the contrast of an image at inter-object level and intra-object level and then
combines it to an enhanced image. The results are compared with various classical methods using image
quality measures
Contrast Enhancement Techniques: A Brief and Concise ReviewIRJET Journal
The document discusses various contrast enhancement techniques for digital images. It provides an overview of techniques such as histogram equalization, which works by flattening the histogram and stretching the dynamic range of gray levels. Global histogram equalization uses the entire image histogram for contrast adjustment, while local histogram equalization processes the image in blocks to better preserve local details and brightness levels. The techniques aim to improve image quality by increasing luminance differences between foreground and background.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Content adaptive single image interpolation based Super Resolution of compres...IJECEIAES
Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling. This paper focuses on upscaling of compressed images with interpolation based Single Image Super Resolution technique. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super Resolution methods. The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on a single image and uses the content of the original image only; therefore, the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method.
7 ijaems sept-2015-8-design and implementation of fuzzy logic based image fus...INFOGAIN PUBLICATION
The quality of image holds importance for both humans and machines. To fulfill the requirement of good quality images, image enhancement is needed. Application of a single contrast enhancement technique often does not produce desirable result and may lead to over enhanced images. To overcome this problem image fusion is performed so that better results with desired enhancement can be achieved. In the present paper an amalgamation of image enhancement, fusion and sharpening have been carried out in the candidate algorithm. The algorithm makes use of fuzzy logic for weight calculation. The results are compared with DACE/LIF approach and it is observed that the proposed algorithm improves the result in terms of quality parameters like PSNR (Peak Signal to Noise Ratio), AMBE (Absolute Mean Brightness Error) and SSIM (Structural Similarity Index) by 0.5 dB, 3 and 0.1 respectively from the existing technique.
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.
Analysis of collaborative learning methods for image contrast enhancementIAEME Publication
The document describes collaborative learning methods for image contrast enhancement. It begins with background on image enhancement techniques like histogram equalization. It then summarizes an existing collaborative learning method that determines pixel values from multiple randomly sampled windows. The document proposes a modified method that combines collaborative learning with block-based histogram equalization using randomly sized sliding windows. It is evaluated on medical and underwater images and is found to provide better results than the original collaborative learning method. Quality metrics are used to measure enhancement.
Quality Assessment of Gray and Color Images through Image Fusion TechniqueIJEEE
. Image fusion is an emerging trend in the digital image processing to enhance images. In image fusion two or more images can be fused (combined) to obtain an enhanced image. In the present work image fusion technology has been used to enhance a given input image. Image fusion is used here to combine two images which contains complementary information.
Performance Evaluation of Filters for Enhancement of Images in Different Appl...IOSR Journals
This document evaluates the performance of different filters for enhancing images in various application areas. It discusses contrast stretching and histogram equalization as common spatial domain techniques for contrast enhancement. Bi-histogram equalization was introduced to preserve image brightness during contrast enhancement. However, contrast enhancement also enhances noise, causing blurriness. The proposed BHEGF method aims to reduce this while providing more accurate results. The document uses median, Gaussian, average, and motion filters and evaluates them based on processing time, mean squared error, brightness count, and peak signal-to-noise ratio to determine which filter provides the best performance for image enhancement.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
A MODIFIED HISTOGRAM BASED FAST ENHANCEMENT ALGORITHMcsandit
The contrast enhancement of medical images has an important role in diseases diagnostic,
specially, cancer cases. Histogram equalization is considered as the most popular algorithm for
contrast enhancement according to its effectiveness and simplicity. In this paper, we present a
modified version of the Histogram Based Fast Enhancement Algorithm. This algorithm
enhances the areas of interest with less complexity. It is applied only to CT head images and its
idea based on treating with the soft tissues and ignoring other details in the image. The
proposed modification make the algorithm is valid for most CT image types with enhanced
results.
Modified Contrast Enhancement using Laplacian and Gaussians Fusion Techniqueiosrjce
The aim of image fusion is to mix images of a scene captured below totally different illumination. One
image contains most of information from the whole supply images automatically. Contrast enhancement is employed
to enhance the standard of visible image with none introducing unrealistic visual appearances. Fusion technique is
employed for the important applications like medical imaging, microscopic imaging, remote sensing, and laptop
vision and robotics. Contrast enhancement improves the brightness differences within the dark, gray or bright regions
at the expense of the brightness differences within the alternative regions. During this paper methodology of the
contrast enhancement for images that improves the local image contrast by controlling the local image gradient. The
proposed methodology improves the improvement drawback and maximizes the local contrast and global contrast of
an image.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
In this project we have implemented a tool to inpaint selected regions from an image. Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information. The tool provides a user interface wherein the user can open an image for inpainting, select the parts
of the image that he wants to reconstruct. The tool would then automatically inpaint the selected area according to the background information. The image can
then be saved. The inpainting in based on the exemplar based approach. The basic aim of this approach is to find examples (i.e. patches) from the image and
replace the lost data with it. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like
dates, subtitles etc.; and the removal of entire objects from the image like microphones or wires in special effects.
Image in Painting Techniques: A survey IOSR Journals
This document provides a survey of different image inpainting techniques. It discusses approaches such as texture synthesis based inpainting, PDE (partial differential equation) based inpainting, exemplar based inpainting, hybrid inpainting, and semi-automatic inpainting. Texture synthesis approaches recreate textures within missing regions by sampling from surrounding textures. PDE based methods diffuse image information into missing areas. Exemplar based techniques iteratively copy patches from surrounding regions. Hybrid methods combine approaches. The document analyzes strengths and limitations of each technique.
The document reviews approaches to image interpolation and super-resolution. It discusses several interpolation methods including polynomial-based, edge-directed, and soft-decision approaches. Edge-directed methods aim to preserve edge sharpness during upsampling by estimating edge orientations or fusing multiple orientations. New edge-directed interpolation uses a Wiener filter to estimate missing pixel values. Soft-decision adaptive interpolation and robust soft-decision interpolation further improve results by modeling image signals within local windows and incorporating outlier weighting. The document provides formulations and comparisons of these methods.
This document summarizes research on image enhancement techniques in the spatial domain. It begins by introducing two categories of image enhancement - spatial domain and frequency domain methods. It then reviews various histogram equalization techniques for contrast enhancement in grayscale images, including global, local, and block-based approaches. The document also discusses extensions of histogram equalization as well as techniques that consider color images and use fuzzy logic approaches. It concludes that histogram equalization is commonly used for contrast enhancement but has limitations, and fuzzy logic methods show promise for color image enhancement.
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A study of a modified histogram based fast enhancement algorithm (mhbfe)sipij
Image enhancement is one of the most important issues in low-level image processing. The goal of image
enhancement is to improve the quality of an image such that enhanced image is better than the original
image. Conventional Histogram equalization (HE) is one of the most algorithms used in the contrast
enhancement of medical images, this due to its simplicity and effectiveness. However, it causes the
unnatural look and visual artefacts, where it tends to change the brightness of an images. The Histogram
Based Fast Enhancement Algorithm (HBFE) tries to enhance the CT head images, where it improves the
water-washed effect caused by conventional histogram equalization algorithms with less complexity. It
depends on using full gray levels to enhance the soft tissues ignoring other image details. We present a
modification of this algorithm to be valid for most CT image types with keeping the degree of simplicity.
Experimental results show that The Modified Histogram Based Fast Enhancement Algorithm (MHBFE)
enhances the results in term of PSNR, AMBE and entropy. We use also the Statistical analysis to ensure
the improvement of the proposed modification that can be generalized. ANalysis Of VAriance (ANOVA) is
used as first to test whether or not all the results have the same average. Then we find the significant
improvement of the modification.
Enhancement of Medical Images using Histogram Based Hybrid TechniqueINFOGAIN PUBLICATION
Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform.
A MODIFIED HISTOGRAM BASED FAST ENHANCEMENT ALGORITHMcscpconf
The contrast enhancement of medical images has an important role in diseases diagnostic,
specially, cancer cases. Histogram equalization is considered as the most popular algorithm for
contrast enhancement according to its effectiveness and simplicity. In this paper, we present a
modified version of the Histogram Based Fast Enhancement Algorithm. This algorithm
enhances the areas of interest with less complexity. It is applied only to CT head images and its
idea based on treating with the soft tissues and ignoring other details in the image. The
proposed modification make the algorithm is valid for most CT image types with enhanced
results.
Histogram Equalization with Range Offset for Brightness Preserved Image Enhan...CSCJournals
In this paper, a simple modification to Global Histogram Equalization (GHE), a well known digital image enhancement method, has been proposed. This proposed method known as Histogram Equalization with Range Offset (HERO) is divided into two stages. In its first stage, an intensity mapping function is constructed by using the cumulative density function of the input image, similar to GHE. Then, during the second stage, an offset for the intensity mapping function will be determined to maintain the mean brightness of the image, which is a crucial criterion for digital image enhancement in consumer electronic products. Comparison with some of the current histogram equalization based enhancement methods shows that HERO successfully preserves the mean brightness and give good enhancement to the image.
A GENERAL STUDY ON HISTOGRAM EQUALIZATION FOR IMAGE ENHANCEMENTpharmaindexing
The document discusses several methods for image enhancement using histogram equalization. It begins with an introduction to histogram equalization and its use in increasing image quality and local contrast. It then reviews three existing histogram equalization methods - Bi-Histogram Equalization with Neighborhood Metrics, Class-Based Parametric Approximation to Histogram Equalization, and Texture Enhanced Histogram Equalization Using TV-L1 Image Decomposition. Each aimed to improve on traditional histogram equalization by addressing issues like maintaining brightness, preserving local information, and avoiding intensity saturation artifacts. The document concludes that variational approaches like TV-L1 decomposition have potential to outperform conventional histogram equalization methods for contrast enhancement.
1) The document proposes a method for color image enhancement using Laplacian pyramid decomposition and histogram equalization. It separates an input image into red, green, and blue color channels.
2) Each color channel is decomposed into a Laplacian pyramid, and histogram equalization is applied to enhance the contrast in each band-pass image.
3) The enhanced band-pass images are then recombined using the Laplacian pyramid reconstruction equation to produce enhanced color channels, which are combined to generate the output enhanced color image. The method aims to improve both local and global contrast while maintaining natural image quality.
1) The document proposes a method for color image enhancement using Laplacian pyramid decomposition and histogram equalization. It separates an input image into red, green, and blue color channels.
2) Each color channel is decomposed into a Laplacian pyramid, and histogram equalization is applied to enhance the contrast in each level. The enhanced levels are then recombined to improve both local and global contrast.
3) The method aims to overcome issues with traditional histogram equalization like over-enhancement, by applying a smoothing technique before contrast adjustment in each level of the pyramid. The final enhanced image is reconstructed by combining the processed color channels.
A MODIFIED HISTOGRAM BASED FAST ENHANCEMENT ALGORITHMcsandit
This document proposes a modified version of the Histogram Based Fast Enhancement Algorithm to improve contrast enhancement of medical images like CT scans. The key modifications are:
1) Calculating the value of k, which determines how many gray levels are ignored, as a ratio of the mean, median, or mode of the histogram rather than a constant value. This makes k adaptive to each image.
2) Applying the modified algorithm to a wide range of CT image types, not just head images, to validate it for more cases.
3) Evaluating the modified algorithm using metrics like PSNR, AMBE, and entropy, as well as visual inspection. Results show the modified algorithm achieves better contrast enhancement
Comparison of Histogram Equalization Techniques for Image Enhancement of Gray...IJMER
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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.
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.
An image enhancement method based on gabor filtering in wavelet domain and ad...nooriasukmaningtyas
The images are not always good enough to convey the proper information.
The image may be very bright or very dark sometime or it may be low
contrast or high contrast. Because of these reasons image enhancement plays
important role in digital image processing. In this paper we proposed an
image enhancement technique in which gabor and median filtering is
performed in wavelet domain and adaptive histogram equalization is
performed in spatial domain. Brightness and contrast are the two parameters
Keywords: used for analyzing the performance of the proposed method.
The document discusses techniques for contrast enhancement of digital images through histogram processing. It describes histogram equalization, which increases contrast by spreading out the most frequent intensity values. Limitations include changes to image brightness. Bi-histogram and multi-histogram equalization partition histograms to minimize brightness changes. Brightness preserving dynamic fuzzy histogram equalization further improves brightness preservation through fuzzy histogram computation, dynamic equalization of histogram partitions, and normalization of image brightness. It provides objective metrics to evaluate contrast enhancement and brightness preservation capabilities of these techniques.
A DISCUSSION ON IMAGE ENHANCEMENT USING HISTOGRAM EQUALIZATION BY VARIOUS MET...pharmaindexing
This document summarizes several papers on image enhancement techniques using histogram equalization. It discusses papers that propose sub-region histogram equalization to improve contrast while preserving spatial relationships. It also discusses a 3D histogram equalization method that produces a uniform 1D grayscale histogram to overcome issues with previous color histogram methods. Another paper proposes using total variation minimization for cartoon-texture decomposition prior to histogram equalization to reduce intensity saturation effects. Further, a technique called gain controllable clipped histogram equalization is presented to enhance contrast while preserving original brightness. Finally, a method called bi-histogram equalization with neighborhood metrics is described which divides histograms to improve local contrast while maintaining brightness.
This document summarizes a technique for modified contrast enhancement of images using Laplacian and Gaussian fusion. It begins by discussing contrast enhancement and image fusion techniques. It then proposes a method that takes two input images, decomposes them using Laplacian pyramid decomposition, and computes their Gaussian pyramids. Weights are assigned to levels in each pyramid before fusing the images to produce a contrast-enhanced output image with improved brightness and visibility of details. The technique aims to enhance local contrast while preserving global features.
This document presents a hybrid color image enhancement technique based on contrast stretching and peak-based histogram equalization. It aims to improve low contrast and reduce noise in medical images to assist with diagnosis of acute leukemia. The technique involves:
1. Applying Gaussian filtering to reduce noise
2. Segmenting the image histogram into regions based on valley points
3. Applying partial contrast enhancement separately to each region to improve morphological features and spread pixels more evenly across intensity values.
The technique is tested on acute leukemia images to enhance visibility of features and ease classification of acute lymphoblastic versus acute myelogenous leukemia, which could help hematologists in analysis. Results show the partial contrast method best improves image visibility while preserving significant features
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MODIFIED HISTOGRAM EQUALIZATION FOR IMAGE CONTRAST ENHANCEMENT USING PARTICLE SWARM OPTIMIZATION
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
DOI : 10.5121/ijcseit.2011.1502 13
MODIFIED HISTOGRAM EQUALIZATION FOR IMAGE
CONTRAST ENHANCEMENT USING PARTICLE
SWARM OPTIMIZATION
P.Shanmugavadivu1
, K.Balasubramanian2
and K.Somasundaram3
1,3
Department of Computer Science and Applications, Gandhigram Rural Institute –
Deemed University, Gandhigram, India,
1
psvadivu67@gmail.com, 3
somasundaramk@yahoo.com
2
Department of Computer Applications, PSNA College of Engineering and Technology,
Dindigul, India, ksbala75@gmail.com
ABSTRACT
A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this
paper. This technique modifies the probability density function of an image by introducing constraints prior
to the process of histogram equalization (HE). These constraints are formulated using two parameters
which are optimized using swarm intelligence. This technique of contrast enhancement takes control over
the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment
factor is then added to the result to normalize the change in the luminance level after enhancement. This
factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of
highly deviated intensities have greater impact in changing the contrast of an image. This approach
provides a convenient and effective way to control the enhancement process, while being adaptive to
various types of images. Experimental results show that the proposed technique gives better results in
terms of Discrete Entropy and SSIM values than the existing histogram-based equalization methods.
KEYWORDS
Contrast Enhancement, Histogram, Histogram Equalization (HE), Probability Density Function (PDF),
Cumulative Density Function (CDF), Swarm Intelligence
1. INTRODUCTION
Contrast enhancement plays a vital role in image processing for both human and computer vision.
It is used as a preprocessing step in medical image processing, texture synthesis, speech
recognition and many other image/video processing applications [1] - [3]. Different techniques
have already been developed for this purpose. Some of these methods make use of simple linear
or nonlinear gray level transformation functions [4] while the rest use complex analysis of
different image features such as edge, connected component information and so on.
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
14
Histogram is a statistical probability distribution of each gray level in a digital image. The
histogram-based equalization techniques are classified into two principal categories as global and
local histogram equalization.
Global Histogram Equalization (GHE) uses the histogram information of the entire input image in
its transformation function. Though this global approach is suitable for overall enhancement, it
fails to preserve the local brightness features of the input image. Normally in an image, the high
frequency gray levels dominate the low frequency gray levels. In this situation, GHE remaps the
gray levels in such a way that the contrast stretching is restricted to some dominating gray levels
having larger image histogram components and causes significant contrast loss for the rest of
them.
Local histogram equalization (LHE) [4] tries to eliminate such problems. It uses a small window
that slides through every pixel of the image sequentially. The block of pixels that are masked by
the window are considered for HE. Then, the gray level mapping for enhancement is done only
for the center pixel of that window. Thus, it makes use of the local information remarkably.
However, LHE requires high computational cost and sometimes causes over-enhancement in
some portions of the image. Moreover, this technique has the problem of enhancing the noises in
the input image along with the image features. The high computational cost of LHE can be
minimized using non-overlapping block based HE. Nonetheless, these methods produce an
undesirable checkerboard effects on enhanced images.
Histogram Equalization (HE) is a very popular technique for contrast enhancement of images [1]
which is widely used due to its simplicity and is comparatively effective on almost all types of
images. HE transforms the gray levels of the image, based on the probability distribution of the
input gray levels. Histogram Specification (HS) [4] is an enhancement technique in which the
expected output of image histogram can be controlled by specifying the desired output histogram.
However, specifying the output histogram pattern is not a simple task as it varies with images. A
method called Dynamic Histogram Specification (DHS) [5] generates the specified histogram
dynamically from the input image. Though this method preserves the original input image
histogram characteristics, the degree of enhancement is not significant.
Brightness preserving Bi-Histogram Equalization (BBHE) [6], Dualistic Sub-Image Histogram
Equalization (DSIHE) [7] and Minimum Mean Brightness Error Bi-Histogram Equalization
(MMBEBHE) [8] are the variants of HE based contrast enhancement. BBHE divides the input
image histogram into two parts, based on the mean brightness of the image and then each part is
equalized independently. This method tries to overcome the problem of brightness preservation.
DSIHE method uses entropy value for histogram separation. MMBEBHE is an extension of
BBHE method that provides maximal brightness preservation. Though these methods can
perform good contrast enhancement, they also cause annoying side effects depending on the
variation of gray level distribution in the histogram. Recursive Mean-Separate Histogram
Equalization (RMSHE) [9], [10], Recursive Sub-image Histogram Equalization (RSIHE) [11]
and Recursively Separated and Weighted Histogram Equalization (RSWHE) [12] are the
improved versions of BBHE. However, they are also not free from side effects. To achieve
sharpness in images, a method called Sub-Regions Histogram Equalization (SRHE) is recently
proposed [13] in which the image is partitioned based on the smoothed intensity values, obtained
by convolving the input image with Gaussian filter. In this paper, we propose a Modified
Histogram Equalization (MHE) method by extending Weighted Thresholded HE (WTHE) [14]
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
15
method for contrast enhancement. In order to obtain the optimized weighing constraints, Particle
Swarm Optimization (PSO) [16] is employed.
Section 2 discusses the popular HE techniques. In section 3, the principle of the proposed method
is presented. Section 4 presents the information about two widely used statistical techniques to
assess image quality. The basic principle of PSO and the procedure to obtain optimized weighing
constraints using PSO are described in section 5. The results and discussions are given in section
6 and in section 7 the conclusion is given.
2. HE TECHNIQUES
In this section, the existing HE approaches such as GHE, LHE, various partition based HE
methods and WTHE are reviewed briefly.
2.1. Global Histogram Equalization (GHE)
For an input image F(x, y) composed of discrete gray levels in the dynamic range of [0,L-1], the
transformation function C(rk) is defined as:
∑∑ ==
===
k
i
i
k
i
ikk
n
n
rPrCS
00
)()(
(1)
where 0 ≤ Sk ≤ 1 and k = 0, 1, 2, …, L-1, ni represents the number of pixels having gray level ri ,
n is the total number of pixels in the input image and P(ri) represents the Probability Density
Function of the input gray level ri. Based on the PDF, the Cumulative Density Function is defined
as C(rk). The mapping given in equation (1) is called Global Histogram Equalization or
Histogram Linearization. Here, Sk is mapped to the dynamic range of [0, L-1] by multiplying it
with (L-1).
Using the CDF values, histogram equalization maps an input level k into an output level Hk using
the level-mapping equation:
)()1( kk rCLH ×−= (2)
For the traditional GHE described above, the increment in the output level Hk is given by:
)()1(1 kkkk rPLHHH ×−=−=∆ −
(3)
The increment of level Hk is proportional to the probability of its corresponding level k in the
original image. For images with continuous intensity levels and PDFs, such a mapping scheme
would perfectly equalize the histogram in theory. However, the intensity levels and PDF of a
digital image are discrete in practice. In such a case, the traditional HE mapping is not ideal and it
results in undesirable effects where the intensity levels with high probabilities often become over-
enhanced and the levels with low probabilities get less enhanced and their frequencies get either
reduced or even eliminated in the resultant image.
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
16
2.2. Local Histogram Equalization (LHE)
Local Histogram Equalization (LHE) performs block-overlapped histogram equalization. LHE
defines a sub-block and retrieves its histogram information. Then, histogram equalization is
applied at the center pixel using the CDF of that sub-block. Next, the window is moved by one
pixel and sub-block histogram equalization is repeated until the end of the input image is reached.
Though LHE cannot adapt to partial light information, it still over-enhances certain portions
depending on its window size. However, selection of an optimal block size that enhances all parts
of an image is not an easy task to perform.
2.3. Histogram Partitioning Approaches
BBHE tries to preserve the average brightness of the image by separating the input image
histogram into two parts based on the input mean and then equalizing each of the parts
independently. RMSHE, RSIHE and RSWHE partition the histogram recursively. Here, some
portions of histogram among the partitions cannot be expanded much, while the outside region
expands significantly that creates unwanted artifacts. This is a common drawback of most of the
existing histogram partitioning techniques since they keep the partitioning point fixed throughout
the entire process of equalization. In all the recursive partitioning techniques, it is not an easy job
to fix the optimal recursion level. Moreover, as the recursion level increases, recursive histogram
equalization techniques produce the results same as GHE and it leads to computational
complexity.
2.4. Weighted Thresholded HE (WTHE)
WTHE is a fast and efficient method for image contrast enhancement [14]. This technique
provides a novel mechanism to control the enhancement process, while being adaptive to various
types of images. WTHE method provides a good trade-off between the two features: adaptivity to
different images and ease of control, which are difficult to achieve in GHE-based enhancement
methods. In this method, the probability density function of an image is modified by weighting
and thresholding prior to HE. A mean adjustment factor is then added with the expectation to
normalize the luminance changes. But, while taking the mean of the input and reconstructed
images, the highly deviated intensity valued pixels known as outlier pixels are also taken into
account. This will not effectively control the luminance change in the output image, which is a
major drawback of this method. The outlier pixels are the pixels which are usually less in number
and are having distant intensity values than the other pixels.
3. MODIFIED HISTOGRAM EQUALIZATION (MHE)
The proposed method, MHE is an extension of WTHE which performs histogram equalization
based on a modified histogram. Each original probability density value P(rk) is replaced by a
Constrained PDF value Pc(rk) yielding:
)()1( kck rPLH ×−=∆ (4)
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17
The proposed level mapping algorithm is given below.
Step 1: Input the image F(i, j) with a total number of ‘n’ pixels in the gray level range [0,L-
1]
Step 2: Compute the Probability Density Function (PDF), P(rk) for the gray levels
Step 3: Find the median value of the PDFs, M1
Step 4: Compute the upper constraint ‘Pu’ :
Pu = v * max(PDF) where 0.1 < v < 1.0
Step 5: Set power factor ‘r’ where 0.1 < r < 1.0
Step 6: Set lower constraint factor Pl be as low as 0.0001
Step 7: Compute the constrained PDF, )( kc rP
≤
≤<
−
−
>
=
=
lk
uklu
r
lu
lk
uku
kkc
PrPif
PrPPifP
PP
PrP
PrPifP
rPTrP
)(0
)(*
)(
)(
))(()(
(5)
Step 8: Find the median value of the constrained PDFs, M2
Step 9: Compute the median adjustment factor Mf as:
Mf = M2 – M1
Step 10: Add Mf to the constrained PDFs
Step 11: Compute cumulative probability density function, Cc(F(i,j))
Step 12: Apply the HE procedure (level mapping) as:
)),(()1(),( jiFCLjiF c×−=′ (6)
where F′(i, j) is the enhanced image
The transformation function T(.) in equation (5) transforms all the original PDF values between
the upper constraint Pu and the lower constraint Pl using a normalized power law function with
index r > 0.
In this level-mapping algorithm, the increment for each intensity level is decided by the
transformed histogram given in equation (5). The increment is controlled by adjusting the index r
of the power law transformation function. For example, when r < 1, the power law function gives
a higher weightage to the low probabilities. Therefore, the lower probability levels are preserved
and the possibility of over-enhancement is less. The effect of the proposed method approaches
that of the GHE, when r→1. When r > 1, more weight is shifted to the high-probability levels and
MHE would yield even stronger effect than the traditional HE.
The upper constraint Pu is used to avoid the dominance of the levels with high probabilities when
allocating the output dynamic range. The lower constraint Pl is used to find the levels whose
probabilities are too low. The Pl value is set to be as low as 0.0001. Any pixel having its
probability less than the lower constraint Pl is having very low impact in the process of contrast
6. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
18
enhancement. It can be observed from equation (5) that when r = 1, Pu=1 and Pl = 0, the proposed
MHE reduces to GHE.
After the constrained PDF is obtained from equation (5), the median adjustment factor (Mf) is
calculated by finding the difference between the median value of the constrained PDFs and the
median value of the original PDFs. Then, the Mf is added to the constrained PDFs which will
effectively control the luminance change, since the outlier pixels are ignored while computing the
medians.
The cumulative density function (CDF) is obtained as:
1,...,1,0,)()(
0
−== ∑=
LkformPkC
k
m
cc
(7)
Then, the HE procedure is applied as given in equation (6). This will not cause serious level
saturation (clipping), to the resulting contrast enhanced image.
The two important parameters namely, v and r, used in this algorithm play a vital role in
enhancing the contrast. Both v and r are accepting values in the range from 0.1 to 1.0. In order to
find the optimum values for v and r, particle swarm optimization technique is employed in which
an objective function is defined which will maximize the contrast of an input image. There are
several measures such as Structural Similarity Index Matrix (SSIM) [11], Discrete Entropy (DE)
[15] etc which are used to calculate the degree of image contrast enhancement. One such measure
can be considered to be an objective function. In this paper, it is found that DE is providing better
trade-off than SSIM. Hence, DE has been selected as an objective function. SSIM is used as a
supporting comparative measure.
4. METRICS TO ASSESS IMAGE QUALITY
4.1. Structural Similarity Index Matrix (SSIM)
The Structural Similarity Index Matrix (SSIM) is defined as:
))((
)2)(2(
),(
2
22
1
22
21
CC
CC
YXSSIM
YXYX
XYYX
++++
++
=
σσµµ
σµµ (8)
where X and Y are the reference and the output images respectively; Xµ and Yµ are respective
mean of X and Y, Xσ and Yσ are the standard deviation of X and Y respectively, XYσ is the
square root of covariance of X and Y, whereas C1 and C2 are constants. The SSIM value between
two images X and Y is generally in the range 0 to 1. If X=Y, then the SSIM is equal to 1 which
implies that when the SSIM value is nearing 1, the degree of structural similarity between the two
images are more.
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4.2 Discrete Entropy (DE)
Discrete entropy E(X) measures the richness of details in an image after enhancement. It is
defined as:
∑
=
−=
255
0
2 ))((log)()(
k
kk XpXpXE
(9)
The details of the original input image is said to be preserved in the enhanced image is said to be
preserved in the enhanced image, when the entropy value of the latter is closer to that of the
former.
5. PARTICLE SWARM OPTIMIZATION (PSO)
In the proposed technique, PSO is adopted to find optimal values of ‘v’ and ‘r’. PSO was first
proposed by Eberhart and Kennedy [16]. This technique is a population-based optimization
algorithm. It uses a number of agents (particles) that constitute a swarm moving around in the
search space looking for the best solution. Each particle keeps track of its coordinates in the
solution space which are associated with the best solution (fitness) that has achieved so far by that
particle. This value is called personal best, pbest and another best value that is tracked by the PSO
is the best value obtained so far by any particle in the neighbourhood of that particle, is called
global best, gbest. The basic concept of PSO lies in accelerating each particle towards its pbest
and the gbest locations, with a random weighted accelaration at each time as shown in Fig. 1.
Figure 1. Modification of a searching point by PSO
Sk
: current searching point
Sk+1
: modified searching point
Vk
: current velocity
Vk+1
: modified velocity
Vpbest
: velocity based on pbest
Vgbest
: velocity based on gbest
8. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
20
Each particle tries to modify its position using the following information: the current position, the
current velocity, the distance between the current position and pbest, the distance between the
current position and the gbest. Each particle’s velocity can be modified using the equation (10).
)(())(() 21
1 k
ii
k
ii
k
i
k
i sgbestrandcspbestrandcwVV −××+−××+=
+
(10)
where, vi
k
is the velocity of agent i at iteration k (usually in the range, 0.1- 0.9); c1 and c2 are the
learning factors in the range, 0 - 4; rand() is the uniformly distributed random number between 0
and 1; si
k
is the current position of agent i at kth
iteration; pbesti is present best of agent i and gbest
is global best of the group. w, the inertia weight is set to be in the range, 0.1 - 0.9 and is computed
as:
Iteration
iterationwMinwMaxwMax
w
max
])[( ×−−
=
(11)
Using the modified velocity, the particle’s position can be updated as:
11 ++
+=
k
i
k
i
k
i VSS (12)
The optimal values of v and r are found using the following procedure:
Step 1: Initialize particles with random position and velocity vector
Step 2: Loop until maximum iteration
Step 2.1: Loop until the particles exhaust
Step 2.1.1: Evaluate the difference between Discrete Entropy values of original
and MHEed image (p)
Step 2.1.2: If p<pbest, then pbest=p
Step 2.2: GOTO Step 2.1
Step 2.3: Set best of pbests as gbest and record the values of v and r
Step 2.4: Update particles velocity using equation (10) and position using equation (12)
Step 3: GOTO Step 2
Step 4: Stop - Giving gbest, the optimal solution with optimal v and r values
6. RESULTS AND DISCUSSION
The performance of the newly developed method, MHE is tested on various standard images such
as Einstein, Village, Bottle, House, Peppers and Truck, out of which Einstein and Village are
given in Fig. 2(a) and 4(a) respectively. To compare the performance of MHE, the same images
are enhanced with the contemporary enhancement techniques like GHE, LHE, BBHE, DSIHE,
HS, RMSHE and WTHE. For all these methods, the performance is measured qualitatively in
terms of human visual perception and quantitatively by computing the DE and SSIM which are
given in Table 1 and Table 2 respectively.
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The original Einstein image and its histogram is respectively given in Fig. 2(a) and 3(a). The
enhanced images of the same by GHE, LHE, BBHE, DSIHE, HS, RMSHE and WTHE are shown
in Fig. 2(b) to 2(h) respectively. It is evident from the visual comparison that BBHE exhibits
better performance than GHE due to its partition-based enhancement. Moreover, it is apparent
from Fig. 2(c) and 2(g) that LHE and RMSHE introduce unwanted artifacts in the enhanced
image. It is to be noted that WTHE (Fig. 2(h)) shows better results in terms of visual perception
when compared to those of GHE, LHE, BBHE, DSIHE, HS and RMSHE. Fig. 2(i) is the result of
the proposed MHE which clearly shows the improvement in image quality than those HE
techniques.
In addition, a clear distinction is noted between the histogram pattern of WTHE (Fig. 3(h)) and
MHE (Fig. 3(i)). This difference is due to the equalization of a range of pixels of the input image
which are ignored by WTHE. Similarly, MHE is found to produce better results for the Village
image in terms of visual perception, compared to other methods as shown in Fig. 4(b) – 4(i)
respectively. The histogram patterns of those images when applied with various HE methods
including MHE are shown in Fig. 3(a) – 3(i) and Fig. 5(a) – 5(i) respectively. The DE values
obtained for various test images are furnished in Table 1. It is evident that for all these images,
the DE values of MHE is found to be higher than all other contemporary methods. Moreover,
MHE is found to generate higher SSIM values for all those images than its peers and enlisted in
Table 2. Hence, the results of the study clearly reveal that MHE produces better image
enhancement compared to the contemporary methods in terms of DE and SSIM.
Table 1. Comparison of Discrete Entropy values of various methods and MHE
Table 2. Comparison of SSIM values of various methods and MHE
10. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
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7. CONCLUSIONS
The proposed contrast enhancement technique, MHE is proved to be a better approach for low
contrast images. Experimental results on standard images have shown that the degree of
enhancement of MHE, measured in terms of DE and SSIM is higher than those of the existing
histogram-based equalization techniques. Moreover, this method is proved to preserve the details
of the input images during enhancement. Hence, this method finds wider application in the fields
including video processing, medical image processing and consumer electronics.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 2. Einstein image (a) Original; results of (b) GHE (c) LHE with window size = 5
(d) BBHE(e) DSIHE (f) HS (g) RMSHE (r=2) (h) WTHE (i) MHE
11. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
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(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 3. Histogram Patterns of Einstein image (a) Original; results of (b) GHE (c) LHE with
window size = 5 (d) BBHE (e) DSIHE (f) HS (g) RMSHE (r=2) (h) WTHE (i) MHE
12. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
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(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 4. Village image (a) Original; results of (b) GHE (c) LHE with window size = 5 (d)
RMSHE (r=2) (e) HS (f) DSIHE (g) BBHE (h) WTHE (i) MHE
13. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
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(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 5. Histogram Patterns of Village image (a) Original (b) GHE (c) LHE with window size=5
(d) BBHE (e) DSIHE (f) HS (g) RMSHE (r=2) (h) WTHE (i) MHE
14. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
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REFERENCES
[1] Wahab, S. H. Chin and E. C. Tan, “Novel approach to automated fingerprint recognition”, IEEE
Proceedings on Vision, Image and Signal Processing vol. 145, no. 3, pp. 160–166, 1998.
[2] S.C. Pei, Y. C. Zeng and C. H. Chang, “Virtual restoration of ancient Chinese paintings using color
contrast enhancement and lacuna texture synthesis”, IEEE Transactions on Image Processing; vol. 13,
no. 3, pp. 416–429, 2004.
[3] Torre, A. M. Peinado, J. C. Segura, J. L. Perez-Cordoba, M. C. Benitez and A. J. Rubio, “Histogram
equalization of speech representation for robust speech recognition”, IEEE Transactions on Speech
Audio Processing, vol. 13, no. 3, pp. 355–366, 2005.
[4] Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing”, 2nd edition, Prentice Hall,
2002.
[5] C.C. Sun, S.J. Ruan, M.C. Shie and T.W. Pai, “Dynamic contrast enhancement based on histogram
specification”, IEEE Transactions on Consumer Electronics, vol. 51, no. 4, pp. 1300–1305, 2005.
[6] Y. Kim, “Contrast enhancement using brightness preserving bihistogram equalization”, IEEE
Transactions on Consumer Electronics, vol. 43, no. 1, pp. 1-8, 1997.
[7] Y. Wan, Q. Chen, and B. Zhang, “Image enhancement based on equal area dualistic sub-image
histogram equalization method”, IEEE Transactions on Consumer Electronics, vol. 45, no. 1, pp. 68-
75, 1999.
[8] S. Chen and A. R. Ramli, “Minimum mean brightness error bi-histogram equalization in contrast
enhancement,” IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1310-1319, 2003.
[9] S. Chen and A. R. Ramli, “Contrast Enhancement using Recursive Mean-Separate Histogram
Equalization for Scalable Brightness Preservation”, IEEE Transactions on Consumer Electronics, vol.
49, no. 4, pp. 1301-1309, 2003.
[10] S. Chen and A. R. Ramli, “Preserving brightness in histogram equalization based contrast
enhancement techniques”, Digital Signal Processing, vol. 14, pp. 413–428, 2004.
[11] K. S. Sim, C. P. Tso, and Y, Y. Tan, “Recursive sub-image histogram equalization applied to gray-
scale images”, Pattern Recognition Letters, vol. 28, pp. 1209-1221, 2007.
[12] M. Kim and M. G. Chung, “Recursively Separated and Weighted Histogram Equalization for
Brightness Preservation and Contrast Enhancement”, IEEE Transactions on Consumer Electronics,
vol. 54, no. 3, pp. 1389-1397, 2008.
[13] H. Ibrahim and N. S. P. Kong, “Image Sharpening using Sub-Regions Histogram Equalization”, IEEE
Transactions on Consumer Electronics, vol. 55, no. 2, pp. 891-895, 2009.
[14] Q. Wang and R. K. Ward, “Fast image/video contrast enhancement based on weighted thresholded
histogram equalization”, IEEE Transactions on Consumer Electronics, vol. 53, no. 2, pp. 757-764,
2007.
[15] Chao Wang and Zhongfu Ye, “Brightness preserving histogram equalization with maximum entropy:
A variational perspective”, IEEE Transactions on Consumer Electronics, vol. 51, pp. 1326-1334,
2005.
[16] J. Kennedy and R. Eberhart, “Particle swarm optimization”, IEEE International Conference on Neural
Networks, vol. 4, pp.1942-1948, 1995.
15. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.5, December 2011
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Authors
P.Shanmugavadivu received her Masters degree in Computer Applications from Regional
Engineering College (now known as NIT), Trichy, 1990 and Ph.D. from the Department of
Computer Science and Applications, Gandhigram Rural Institute-Deemed University,
Gandhigram, India, 2008 and is working as Associate Professor in the same department.
Her research interests are image restoration, image segmentation, image enhancement and
data mining.
K. Balasubramanian graduated from Madurai Kamaraj University and post graduated from
Bharathidasan University, Trichy, India. He is currently pursuing Ph.D. at Gandhigram
Rural Institute-Deemed University, Gandhigram, India. He is currently working as
Associate Professor at PSNA College of Engineering and Technology, Dindigul, India. His
areas of interest include Digital Image Processing and Web Technology.
K. Somasundaram received Ph.D. in Theoretical Physics from IISc., Bangalore, India,
1984. He is presently the Professor and Head of the Dept. of Computer Science and
Applications at Gandhigram Rural Institute-Deemed University, Gandhigram, India. His
research interests are image enhancement, image compression and medical imaging.