%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 = {2019-08-01T08:06:45.000+0200}, author = {P.Shanmugavadivu and K.Balasubramanian and K.Somasundaram}, biburl = {https://www.bibsonomy.org/bibtex/2a7769baa549585e686649a35b280bf40/ijcseit}, doi = {10.5121/ijcseit.2011.1502}, interhash = {c7bec82348d81ef2765f2838c56816bf}, intrahash = {a7769baa549585e686649a35b280bf40}, issn = {0974 - 9330 (Online); 0975 - 2307 (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 = {2019-08-01T08:06:45.000+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 }