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Modified Multipeak Histogram Equalization for Brightness Preserving Image Enhancement

Ashwini S. Zadbuke

Abstract


This paper proposes a new modified method, known as modified Multipeak histogram equalization (MMPHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with nearest neighborhood averaging filter, and then partitions the smoothed histogram based on its local maximums Next, each partition will be assigned to a new dynamic range. After that, the modified step added that is histogram normalization, after normalization, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will maintain the mean brightness of the image. . Our results from 20 test images shows that this method performs well of other present mean brightness preserving histogram equalization methods


Keywords


Image Contrast Enhancement, Histogram Equalization, Averaging Filter, Histogram Normalization, AMBE, ENTROPY, PSNR, Brightness Preserving Enhancement.

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References


A. Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing,” 2nd edition, Prentice Hall, 2002.

Joung-Youn Kim, Lee-Sup Kim and Seung-Ho Hwang, “An advanced contrast enhancement using partially overlapped sub-block histogram equalization,” IEEE Trans. Circuits Syst. Video Technol., vol. 11, no. 4, pp. 475-484, Apr. 2001.

Yeong-Taeg Kim, “Contrast enhancement using brightness preserving bi histogram equalization,” IEEE Trans. Consumer Electronics, vol. 43, no. 1, pp. 1-8, Feb. 1997.

Yu Wan, Qian Chen and Bao-Min Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization March,03,1982

Soong-Der Chen and Abd. Rahman Ramli, “Minimum mean brightness error bi-histogram equalization in contrast enhancement,” IEEE Trans. Consumer Electron., vol. 49, no. 4, pp. 1310-1319, Nov. 2003.

Soong-Der Chen and Abd. Rahman Ramli, “Contrast enhancement using recursive mean-separate histogram equalization for scalable

Brightness preservation,” IEEE Trans. Consumer Electron., vol. 49, no.4, pp. 1301-1309, Nov. 2003.

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, no. 10, pp. 1209-1221, 2007.

D. Menotti, L. Najman, J. Facon and A. A. Araujo, “Multi-peak histogram equalization methods for contrast enhancement and brightness.

H. Ibrahim and N. S. P. Kong, “Brightness preserving dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consumer Electron., vol. 53, no. 4, pp. 1752-1758, Nov. 2007.

Nyamlkhagva Sengee and Heung Kook Choi, “Brightness Preserving Weight Clustering Histogram Equalization,” IEEE Trans. Consumer Electron., vol. 54, no. 3, pp. 1329-1337, Aug. 2008.

Hojat Yeganeh, Ali Ziaei and Amirhossein Rezaie, “A novel approach for contrast enhancement based on histogram equalization,” In Proceedings of the International Conference on Computer and Communication Engineering, pp. 256-260, 2008.

Aghagolzadeh and O. K. Ersoy, “Transform image enhancement,” Opt. Eng., vol. 31, pp. 614626, Mar. 1992.

J. Tang, E. Peli and S. Acton, “Image enhancement using a contrast measure in the compressed domain,” IEEE Signal Process. Let., vol. 10, no. 10, pp. 289-292, Oct. 2003.

S. Lee, “An efficient content-based image enhancement in the compressed domain using retinex theory,” IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 2, pp. 199-213, sep. 2007.

Jayanta Mukherjee and Sanjit K. Mitra, “Enhancement of color images by scaling the DCT Coefficients,” IEEE Trans. Image compressed domain using retinex theory,” IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 2, pp. 199-213, Feb. 2007.

X. Zong, A. F. Laine, E. A. Geiser and D. C. Wilson, “Denoising and contrast enhancement via wavelet shrinkage and nonlinear adaptive gain,” Proc. of SPIE, vol. 2762, pp. 566-574, Orlando, Apr. 1996.

K. V. Velde, “Multi-scale color image enhancement,” in Proc. Int. Conf Image Processing, vol. 3, pp. 584-587, 1999.

Y. Wan and D. Shi, “Joint exact histogram specification and image enhancement through the wavelet transform,” IEEE Trans. Image Process., vol. 17, no. 10, pp. 1783-1794, Oct. 2008.

J. Starck, F. Murtagh, E. J. Cands and D. L. Donoho, “Gray and color image contrast enhancement by the curvelet transform,” IEEE Trans. Image process., vol. 12, no. 6, pp. 706-717, Jun. 2003.

Qingwu Li, Xue Ni and Guogao Liu, “Ceramic image processing using the second generation curvelet transform and watershed

algorithm,” In Proceedings of the International Conference on Robotics and Biomimetics, pp. 2037-2042, 2007.

Ehsan Nezhadarya and Mohammad B. Shamsollahi, “Image contrast enhancement by contourlet transform,” In Proceedings of the 48th International Symposium, pp. 81-84, Jun. 2006.

D. L. Donoho and M. R. Duncan, “Digital curvelet transform: Strategy, implementation and experiments,” Proc. SPIE, vol. 4056,

pp. 12-29, 2000.

E. J. Cand`es, L. Demanet, D. L. Donoho and L. Ying, “Fast discrete curvelet transforms,” SIAM Multiscale Modeling Simul., 2006.


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