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.
%0 Journal Article
%1 noauthororeditor
%A P.Shanmugavadivu,
%A K.Balasubramanian,
%A K.Somasundaram,
%D 2011
%J International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)
%K (CDF) (HE) (PDF) Contrast Cumulative Density Enhancement Equalization Function Histogram Intelligence Probability Swarm
%N 5
%P 13-27
%R 10.5121/ijcseit.2011.1502
%T MODIFIED HISTOGRAM EQUALIZATION FOR IMAGE
CONTRAST ENHANCEMENT USING PARTICLE
SWARM OPTIMIZATION
%U http://airccse.org/journal/ijcseit/papers/1211ijcseit02.pdf
%V 1
%X 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.
@article{noauthororeditor,
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. },
added-at = {2018-09-14T11:49:08.000+0200},
author = {P.Shanmugavadivu and K.Balasubramanian and K.Somasundaram},
biburl = {https://www.bibsonomy.org/bibtex/2151337713d033c5f510ded99f54fc2ba/ijcseit},
doi = {10.5121/ijcseit.2011.1502},
interhash = {c7bec82348d81ef2765f2838c56816bf},
intrahash = {151337713d033c5f510ded99f54fc2ba},
issn = {2231-3117 [Online] ; 2231-3605 [Print]},
journal = {International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)},
keywords = {(CDF) (HE) (PDF) Contrast Cumulative Density Enhancement Equalization Function Histogram Intelligence Probability Swarm},
language = {English},
month = dec,
number = 5,
pages = {13-27},
timestamp = {2018-09-14T11:49:08.000+0200},
title = {MODIFIED HISTOGRAM EQUALIZATION FOR IMAGE
CONTRAST ENHANCEMENT USING PARTICLE
SWARM OPTIMIZATION
},
url = {http://airccse.org/journal/ijcseit/papers/1211ijcseit02.pdf},
volume = 1,
year = 2011
}