Variance Value Limited Clipping of Pentile based Sub-histogram Equalization for Contrast Enhancement of Image

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Kuldip Acharya 1,* Dibyendu Ghoshal 2

1. Department of Computer Science and Engineering, National Institute of Technology, Agartala, Barjala, Jirania, Tripura (West), India

2. Department of Electronics and Communication Engineering, National Institute of Technology, Agartala, Barjala, Jirania, Tripura (West), India

* Corresponding author.


Received: 17 Feb. 2020 / Revised: 20 Apr. 2020 / Accepted: 2 Jul. 2020 / Published: 8 Dec. 2020

Index Terms

Contrast enhancement, Histogram clipping, Histogram Equalization, Image enhancement, Variance.


Digital image enhancement is a technique to process a digital image to improve the overall visual quality of image. In this paper, Variance concept based clipping threshold value is computed from input image pixel intensity to limit the rate of over enhancement. The histogram of the original image is sub-divided into five adjacent sections and the boundary values between adjacent sections are put from the penile value of intensity range. Besides, over enhancement of the image is avoided by clipping certain number of pixels having more intensity than the clipping limit and these pixels are rearranged below the clipping limit. The present method offers two advantages viz., clipping of the certain pixels based on the property of the data set itself. The another one is to histogram processing by parts and this has given better visual quality, low computation time with improved metrics related to image enhancement. Histogram of each specific sub-image is equalized independently and then combined to produce the final contrast enhanced image. The final output image is further processed through imreducehaze filter for more improve result. Quantitative evaluation of proposed algorithm is performed by CPCQI and QILV image quality metrics and the simulation results have shown that the proposed variance based histogram equalization algorithm produces better quality of image in terms of contrasts, brightness and color in comparison to the other existing histogram equalization algorithms.

Cite This Paper

Kuldip Acharya, Dibyendu Ghoshal, " Variance Value Limited Clipping of Pentile based Sub-histogram Equalization for Contrast Enhancement of Image", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.12, No.6, pp. 33-42, 2020. DOI: 10.5815/ijigsp.2020.06.04


[1] Enhancement methods in image processing,, accessed February 12, 2020

[2] H. R. Sheikh and A. C. Bovik, "Image information and visual quality," in IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, Feb. 2006.

[3] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB. Prentice Hall, 2004.

[4] Kaur, Manpreet, Kaur, Jasdeep, and Kaur, Jappreet. Survey of contrast enhancement techniques based on histogram equalization. International Journal of Advanced Computer Science and Applications, 2(7):137–141, 2011.

[5] Soong-Der Chen, A. Rahman Ramli, “Preserving brightness in histogram equalization-based contrast enhancement techniques,” Digital Signal Processing, 12(5), pp.413-428, September 2004.

[6] K. Singh, R. Kapoor, S. K. Sinha, "Enhancement of low exposure images via recursive histogram equalization algorithms", Optik-Int. J. Light Electron Opt., vol. 126, no. 20, pp. 2619-2625, 2015.

[7] Soong-Der Chen and A. R. Ramli, "Minimum mean brightness error bi-histogram equalization in contrast enhancement," in IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1310-1319, Nov. 2003.

[8] E. Wharton, K. Panetta and S. Agaian, "Human Visual System Based Multi-Histogram Equalization for Non-Uniform Illumination and Shadow Correction," 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, Honolulu, HI, 2007, pp. I-729-I-732.

[9] K. Singh, R. Kapoor, "Image enhancement using exposure-based sub image histogram equalization", Pattern Recognition Letters, vol. 36, pp. 10-14, 2014.

[10] Singh, K., Kapoor, R., Sinha, S.K, ‘Enhancement of low exposure images via recursive histogram equalization algorithms’, Optik., 2015, 126, (20), pp. 2619–2625

[11] Singh, K., Kapoor, R, ‘Image enhancement via median-mean based subimage-clipped histogram equalization’, Optik., 2014, 125, (17), pp. 4646–4651

[12] Al-Ameen, Zohair, ‘Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators’, International Journal of Intelligent Systems and Applications., 2016, 8, (8), pp. 10-17

[13] Matin, F., Jeong, Y., Kim, K., et al, ‘Color image enhancement using multi scale retinex based on particle swarm optimization method’, IOP Conf. Series: Journal of Physics: Conf. Series., 2018, 960, pp. 12026

[14] Jobson, D. J., Rahman, Z., Woodell, G. A, ‘A multiscale retinex for bridging the gap between color images and the human observation of scenes’, IEEE Transactions on Image Processing., 1997, 6, (7), pp. 965-976

[15] Haitao Wang, S. Z. Li and Yangsheng Wang, "Face recognition under varying lighting conditions using self quotient image," Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., Seoul, South Korea, 2004, pp. 819-824.

[16] Arbelaez, P., Maire, M., Fowlkes, C., et al, ‘Contour Detection and Hierarchical Image Segmentation’, IEEE Transactions on Pattern Analysis and Machine Intelligence., 2011, 33, (5), pp. 898-916

[17] Imreducehaze,, accessed February13, 2020

[18] S. Aja-Fernndez, R. San-Jos-Estpar, C. Alberola-Lpez, C.-F. Westin, "Image quality assessment based on local variance", Proc. 28th IEEE EMBC, pp. 4815-4818, 2006-Sep

[19] K. Gu, D. Tao, J.-F. Qiao, and W. Lin, “Learning a no-reference quality assessment model of enhanced images with big data,” IEEE Trans. Neural Netw. Learn. Syst., to be published.

[20] MATLAB and Statistics Toolbox Release 2018a, The MathWorks, Inc., A Natick ed., Massachusetts, United States.