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http://dx.doi.org/10.5762/KAIS.2014.15.7.4475

Weighted Histogram Equalization Method adopting Weber-Fechner's Law for Image Enhancement  

Kim, Donghyung (Dept. of Computer Science & Information Systems, Hanyang Women's Univ.)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.15, no.7, 2014 , pp. 4475-4481 More about this Journal
Abstract
A histogram equalization method have been used traditionally for the image enhancement of low quality images. This uses the transformation function, which is a cumulative density function of an input image, and it has mathematically maximum entropy. This method, however, may yield whitening artifacts. This paper proposes the weighted histogram equalization method based on histogram equalization. It has Weber-Fechner's law for a human's vision characteristics, and a dynamic range modification to solve the problem of some methods, which yield a transformation function, regardless of the input image. Finally, the proposed transformation function was calculated using the weighted average of Weber-Fechner and the histogram equalization transformation functions in a modified dynamic range. The simulation results showed that the proposed algorithm effectively enhances the contrast in terms of the subjective quality. In addition, the proposed method has similar or higher entropy than the other conventional approaches.
Keywords
Histogram Equalization; Image Enhancement; Weber-Fechner's law; Weighted Histogram Equaliztion adopting Weber-Fechner's Law(WHEWF);
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  • Reference
1 R. C. Gonzalez and R. E. Wood, Digital Image Processing, 2nd ed., pp. 75-146, Prentice Hall, 2002.
2 Y. T. Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," IEEE Trans. Consumer Electronics. vol.43, no.1, pp. 1-8, Feb., 1997. DOI: http://dx.doi.org/10.1109/30.580378   DOI   ScienceOn
3 Y. Wan, Q. Chen, and B. M. Zhang, "Image enhancement based on equal area dualistic sub-image histogram equalization method," IEEE Trans. Consumer Electronics, vol.45, no.1, pp.68-75, Feb., 1999. DOI: http://dx.doi.org/10.1109/30.754419   DOI   ScienceOn
4 S. Chen and A. Ramli, "Minimum mean brightness error bi-histogram equalization in contrast enhancement," IEEE Trans. Consumer Electronics, vol.49, no.4, pp.1310-1319, Nov., 2003. DOI: http://dx.doi.org/10.1109/TCE.2003.1261234   DOI   ScienceOn
5 C. Wang, J. Peng, and Z. Ye "Flattest histogram specification with accurate brightness preservation," IEEE Trans. Image Processing, vol.2, no. 5, pp.249-262, Oct. 2008. DOI: http://dx.doi.org/10.1049/iet-ipr:20070198   DOI   ScienceOn
6 S. Yang, J. H. Oh, and Y. Park, "Contrast enhancement using histogram equalization with bin underflow and bin overflow," IEEE Conference on Image Processing, vol.1, pp. 881-884, Sept. 2003.
7 T. Arici, S. Dikbas, and Y. Altunbasak, "A histogram modification framework and its application for image contrast enhancement," IEEE Trans. Image Processing, vol.18, no.9, pp.1921--1935, Sept., 2009. DOI: http://dx.doi.org/10.1109/TIP.2009.2021548   DOI   ScienceOn
8 T. Kim and J. Paik, "Adaptive Contrast Enhancement Using Gain-Controllable Clipped Histogram Equalization," IEEE Trans. Consumer Electonics, vol.54, no.4, Nov., 2008. DOI: http://dx.doi.org/10.1109/TCE.2008.4711238   DOI   ScienceOn
9 K. Murakoshi, M. Miura, "Image correction method for the colour contrast effect using inverse processes of the brain," ELSEVIER Trans. BioSystesm, vol.101, pp.162-165, Sept., 2003. DOI: http://dx.doi.org/10.1016/j.biosystems.2010.06.004   DOI   ScienceOn