Image Contrast Enhancement Using Triple Dynamic Clipped Histogram Equalization

Authors

1 Electrical and Computer Complex, Malek Ashtar University of Technology, Tehran, Iran

2 Faculty of New Sciences and Technology, University of Tehran, Tehran, Iran

Abstract

In this paper, a powerful contrast enhancement algorithm based on the histogram equalization, called triple dynamic clipped histogram equalization (TDCHE) is introduced. The proposed method consists of four main processes including partitioning, clipping, mapping and equalization. At first, the input image histogram is partitioned into three portions with a same number of pixels. Next, the histogram clipping process is performed by comparing the clipped threshold level with the average intensities occurred on each sub-histogram. Then, each sub-histogram is mapped to a new dynamic range using simple calculations and finally, equalization process of each histogram is independently accomplished. The proposed TDCHE technique is presented to achieve multiple objectives of maximum average information content (entropy), enhancement ratio control and maintaining the reasonable brightness. In addition, this method leads to a natural enhancement in the output image by providing sharp images, while preserving maximum details far from unnatural phenomena such as intensity saturation and noise enhancement. Evaluation of the proposed TDCHE method performance in terms of information content as well as visual quality shows perceived superiority of the proposed algorithm in comparison to the previously presented methods based on histogram equalization.

Keywords


[1] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Englewood Cliffs, NJ: Prentice Hall, 2008.
[2] H. Demirel, C. Ozcinar and G. Anbarjafari, “Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition,” IEEE Geoscience and Remote Sensing Letter, vol. 7, no. 2, pp. 333-337, 2010.
[3] T. Kim and J. Paik, “ Adaptive contrast enhancement using gain-controllable clipped histogram equalization,” IEEE Transactions Consumer Electronics, vol. 54, no. 4, pp. 1803–1810, 2008.
[4] Y. T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Transactions Consumer Electronics, vol. 43,no. 1, pp. 1-8, 1997.
[5] Y. Wan, Q. Chen and B.M. Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method,” IEEE Transactions Consumer Electronics, vol. 45, no. 1, pp. 68-75, 1999.
[6] S.D. Chen and R.A. Ramli, “Minimum mean brightness error Bi-histogram equalization in contrast enhancement,” IEEE Transactions Consumer Electronics, vol. 49, no. 4, pp. 1310-1319, 2003.
[7] S.D. Chen and A.R. Ramli, “Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation,” IEEE Transactions Consumer Electronics, vol. 49, no. 4, pp. 1301-1309, 2003.
[8] K. S. Sim, C. P. Tso and Y. Y. Tan, “Recursive sub-image histogram equalization applied to gray scale images,” Elsevier Pattern Recognition Letters, vol. 28, no. 10, pp. 1209-1221, 2007.
[9] M. Kim and M.G. Chung, “Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement,” IEEE Transactions Consumer Electronics, vol. 54, no. 3, pp. 1389-1397, 2008.
[10] K. Wongsritong, K. Kittayaruasiriwat, F. Cheevasuvit, K. Dejhan  and A. Somboonkaew, “  Contrast enhancement using multipeak histogram equalization with brightness preserving,” IEEE Asia-Pasific Conference on Circuit and Systems, pp. 455-458, 1998.
[11] M.A. Al-Wadud,  Md.H. Kabir, M.A.A. Dewan and O. Chae, “A dynamic histogram equalization for image contrast enhancement,” IEEE Transactions Consumer Electronics, vol. 53,no. 2, pp. 593-600, 2007.
[12] H. Ibrahim and N.S.P. Kong, “Brightness preserving dynamic histogram equalization for image contrast enhancement,” IEEE Transactions Consumer Electronics, vol. 53,no. 4, pp. 1752-1758, 2007.
[13] D. Sheet, H. Garud, A. Suveer, M. Mahadevappa  and  J. Chatterjee, ”Brightness pre-serving dynamic fuzzy histogram equalization,” IEEE Transactions. Consumer Electronics, vol. 56,no. 4, pp. 2475–2480, 2010.
[14] C.H. Ooi, N.S.P. Kong and H. Ibrahim, “Bi-histogram with a plateau limit for digital image enhancement,” IEEE Transactions Consumer Electronics, vol. 55, no. 4, pp. 2072–2080, 2009.
[15] C.H. Ooi and N.A.M. Isa, “Quadrants dynamic histogram equalization for contrast enhancement,” IEEE Transactions Consumer Electronics, vol. 56, no. 4, pp. 2552–2559, 2010.
[16] C.H. Ooi and N.A.M. Isa, “Adaptive contrast enhancement methods with brightness preserving,” IEEE Transactions Consumer Electronics, vol. 56,no. 4, pp. 2543–2551, 2010.
[17] K. Singh and R. Kapoor, “Image enhancement via median-mean based sub-image-clipped histogram equalization,” Elsevier Optik, vol. 125, pp. 4646–4651, 2014.
[18] K. Singh and R. Kapoor, “Image enhancement using exposure based sub image histogram equalization,” Elsevier Pattern Recognition Letters, vol. 36, pp. 10-14, 2014.
[19] K. Singh, R. Kapoor and S.K. Sinha, “Enhancement of low exposure images via recursive histogram equalization algorithms,” Elsevier Optik, vol. 126, pp. 2619–2625, 2015. 
[20] C. Wang and Z. Ye, “Brightness preserving histogram equalization with maximum entropy: A variational perspective,” IEEE Transactions Consumer Electronics, vol. 51, no. 4, pp. 1326-1334, 2005.
[21] S.D. Chen, “A new image quality measure for assessment of histogram equalization-based contrast enhancement,” Elsevier Digital Signal Processing, vol. 22, pp. 640–647, 2012.
 [22] مرتضی به نام و حسین پورقاسم،  »شناسایی صرع بر اساس بهینه‌سازی ویژگی‌های ادغامی تبدیل هارتلی با مدل ترکیبیGA و  MLP همراه با استراتژی یادگیری ممتیک،« مجله مهندسی برق دانشگاه تبریز،  جلد ۴۵ ، شماره ۴،  صفحه 67-51، 1394.