Kuldip Acharya; Dibyendu Ghoshal

Contrast Enhancement of Images through Skewness and Mode Based Bi-Histogram Equalization

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Author(s)

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.

DOI: https://doi.org/10.5815/ijigsp.2020.05.02

Received: 25 Feb. 2020 / Revised: 20 May 2020 / Accepted: 2 Jul. 2020 / Published: 8 Oct. 2020

Index Terms

Skewness, Mode, Contrast, Enhancement, Bi-histogram.

Abstract

In this paper, skewness and mode-based histogram equalization algorithm have been proposed for contrast enhancement of digital images. The present method gives a novel idea for histogram clipping and histogram bifurcation. The prior is done with the skewness value and the latter is done with help of mode values of the intensity level random data set. The pixel intensity levels are random and thus a stochastic approach has been used and found to yield improved figure of merits. The image histogram has been clipped with the help of a pre-assigned threshold value computed from skewness value to restrict the rate of over enhancement. The clipped histogram is subdivided into two parts, using the histogram subdivision limit which is calculated on the basis of the mode value of the image. Histogram of individual sub-image is equalized independently and then integrated to form the final enhanced image. The simulation results have shown that the proposed skewness and mode based bi-histogram equalization algorithm enhances the contrast of the image in a better manner compared with the other histogram equalization methods in terms of FSIM, PSIM, SFF, VSI, HaarPSI, and GMSD.

Cite This Paper

Kuldip Acharya, Dibyendu Ghoshal, " Contrast Enhancement of Images through Skewness and Mode Based Bi-Histogram Equalization", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.12, No.5, pp. 13-27, 2020. DOI: 10.5815/ijigsp.2020.05.02

Reference

[1]Gonzalez, R.C., Woods, R.E., “Digital image processing” (Prentice-hall, NJ, USA, 2007)

[2]Chen, S. D., Ramli, A. R., “Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement”, IEEE Trans. Consumer Electronics., 2003, 49, pp. 1310-1319

[3]Kim, K., Kim, S., Kim, K., “Effective image enhancement techniques for fog-affected indoor and outdoor images”, IET Image Processing., 2018, 12, (4), pp. 465-471

[4]Deng, H., Sun, X., Liu, M., et al., “Image enhancement based on intuitionistic fuzzy sets theory”, IET Image Process., 2016, 10, (10), pp. 701–709

[5]Zhang, Y., Liu, H., Huang, N., et al., “Dynamical stochastic resonance for non-uniform illumination image enhancement”, IET Image Proc., December 2018, 12, (12), pp. 2147 – 2152

[6]Paul, A., Bhattacharya, P., Maity, S., et al., “Plateau limit-based tri-histogram equalisation for image enhancement”, IET Image Processing., 2018, 12, (9), pp. 1617-1625

[7]Al-Ameen, Zohair., “Nighttime image enhancement using a new illumination boost algorithm”, IET Image Processing., 2019, 13, (8), pp. 1314-1320

[8]Tang, C., Wang, Y., Feng, H., et al., “Low-light image enhancement with strong light weakening and bright halo suppressing”, IET Image Processing., 2019, 13, (3), pp. 537 – 542

[9]Zarie, M., Pourmohammad, A., Hajghassem, H., “Image contrast enhancement using triple clipped dynamic histogram equalisation based on standard deviation”, IET Image Processing., 2019, 13, (7), pp. 1081 – 1089

[10]Nandal, A., Bhaskar, V., Dhaka, A., “Contrast-based image enhancement algorithm using grey-scale and colour space”, 2018, IET Image Processing., 12, (4), pp.514 – 521

[11]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

[12]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

[13]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

[14]Liu, Y., Yan, H., Ga, S., et al., “Criteria to evaluate the fidelity of image enhancement by MSRCR”, IET Image Processing., 2018, 12, (6), pp. 880-887

[15]kansal, S., Tripathi, R.K., “Adaptive Geometric Filtering Based on Average Brightness of the Image and Discrete Cosine Transform Coefficient Adjustment for Gray and Color Image Enhancement”. Arab J Sci Eng (2019)

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

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

[18]Adelson, E.H., “Image statistics and surface perception”, In Human Vision and Electronic Imaging XIII, Proceedings of the SPIE., 2008, 6806, pp. 680602–680609

[19]Sharan, L., Li, Y., Motoyoshi, I., et al., “Image statistics for surface reflectance perception”, Journal of the Optical Society of America A., 25(4), pp.846–865,2008

[20]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

[21]Zhang, L., Zhang, L., Mou, X., et al., “FSIM: a feature similarity index for image quality assessment”, IEEE Trans. Image Process. 2011, 20 (8), pp. 2378–2386

[22]K. Gu, L. Li, H. Lu, X. Min and W. Lin, "A Fast Reliable Image Quality Predictor by Fusing Micro- and Macro-Structures”, in IEEE Transactions on Industrial Electronics, vol. 64, no. 5, pp. 3903-3912, May 2017.

[23]H. Chang, H. Yang, Y. Gan and M. Wang, "Sparse Feature Fidelity for Perceptual Image Quality Assessment”, in IEEE Transactions on Image Processing, vol. 22, no. 10, pp. 4007-4018, Oct. 2013.

[24]L. Zhang, Y. Shen, H. Li., “VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment”, IEEE Transactions on Image Processing, 2014, 23, (10), pp. 4270-4281

[25]R. Reisenhofer, S. Bosse, G. Kutyniok and T., “Wiegand. A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment”. (PDF) Signal Processing: Image Communication, vol. 61, 33-43, 2018.

[26]Xue, W., Zhang, L., Mou, X., et al., “Gradient magnitude similarity deviation: a highly efficient perceptual image quality index”, IEEE Trans. Image Process., 2014, 23, (2), pp. 684–695

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

International Journal of Image, Graphics and Signal Processing (IJIGSP)