Adaptive Gamma Weighted Tri Histogram Equalisation
Main Article Content
Abstract
This paper introduces a novel image enhancement technique called Adaptive Gamma Weighted Tri Histogram Equalisation (AGWTHE), designed to improve
the visual quality of both grayscale and color images. The proposed method incorporates adaptive gamma correction into a tri histogram equalisation framework, enhancing image contrast while preserving brightness and details. Initially, the image histogram is adaptively clipped using a gamma weighted function to prevent over enhancement and detail loss. The resulting histogram
is then partitioned into three sub histograms based on statistical features such as mean and standard deviation. Each sub histogram undergoes individual histogram
equalisation, followed by a fusion process to generate the final enhanced image. Experimental evaluations demonstrate that AGWTHE effectively enhances image
quality while maintaining natural appearance. The method outperforms several state of the art techniques in both subjective and objective assessments, as validated by metrics including Entropy, FSIM, VSI, and GMSD. The proposed approach is robust, adaptive, and suitable for a wide range of image types and applications.
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References
R.C. Gonzalez, R.E. Woods. Digital image processing, 4th ed. NJ USA: Prentice-hall, 2007.
Y.T. Kim, ”Contrast enhancement using brightness preserving bi-histogram equalisation”, IEEE Trans. Consum. Electron., Vol.43, no.1, pp. 1–8, 1997.
Y. Wang, Q. Chen and B. Zhang, ”Image enhancement based on equal area dualistic subimage histogram equalization method”,IEEE Trans. Consum. Electron., vol.45, no.1, pp. 68–75, 1999.
S. D. Chen and A.R. Ramli, ”Contrast enhancement using recursive mean separate histogram equalisation for scalable brightness preservation”, IEEE
Trans. Consum. Electron., vol.49, no.4, pp. 1301– 1309 2003.
K.S. Sim, C.P. Tso and Y.Y. Tan, ”Recursive subimage histogram equalisation applied to gray scale images”,Pattern Recognit. Lett., vol.28, pp. 1209– 1221, 2007.
M. Kim and M. G. Chung,”Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement”, IEEE Transactions on Consumer Electronics., vol. 54, no.3, pp. 1389-1397,2008.
C.H. Ooi, N.S.P. Kong and H. Ibrahim, ”Bihistogram with a plateau limit for digital image enhancement”, IEEE Trans. Consum. Electron., vol.55, no.4, pp. 2072–2080, 2009.
P.H. Lin, C.C. Lin and H.C. Lin, ”Tri-histogram equalization based on first order statistics”, in 2009 IEEE 13th Int. Symp. on Consumer Electronics, Kyoto, 2009, pp. 387–391.
Qing Wang and K. Rabab Ward, ”Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization”, IEEE Transactions on Consumer Electronics, vol.53, no.2, pp.757-764, 2007.
K. Singh, R. Kapoor, ”Image enhancement using exposure based sub image histogram equalization”, Pattern Recogn Lett, vol.36, pp.10–14, 2014.
S. Kansal, S. Purwar and R. Tripathi,”Image contrast enhancement using unsharp masking and histogram equalization”,Multimed Tools Appl, vol.77, no.20, pp.26919-26938, 2018.
A. Paul, P. Bhattacharya, P. Santi Maity and Bidyut Kr. Bhattacharyya, ”Plateau limit-based trihistogram equalisation for image enhancement” IET Image Process., vol. 12 , no.9, pp. 1617-1625, 2018.
V.K. Yadav and J. Singhai, ”Adaptive gamma correction for automatic contrast enhancement of Chest-X-ray images affected by various lung diseases”,Multimed Tools Appl,vol.83, pp.73457– 73475, 2024.
M. Veluchamy and B. Subramani, ”Image contrast and color enhancement using adaptive gamma correction and histogram equalization”, Optik, vol.183, pp.329–337, 2019.
Z. Huag, T. Zhang and Q. Li, ”Adaptive gamma correction based-on cumulative histogram for enhancing near-infrared images”, Infrared Phys Technol, vol.79, pp.205–215, 2016.
J.F Yang, Y.H. Shi and X.L. Xiong, ”Improved Gamma correction method in weakening illumina illumination”, Journal of Civil Avation Univesity in China, vol. 24, pp. 39-42, 2006.
Incekara,A. H and Seker,D. Z.: ’Investigating the Distinctive Character of Intensity Values in Spatially Enhancement of Historical Aerial Photographs Using Adaptive Gamma Correction’, IEEE Geoscience and Remote Sensing Letters, 2025, vol. 22, pp. 1-5.
Zhang, J., Cui, X., Li, J., Jiang, B., Li, L. and Dai,S., ’DBAC: A Novel Method for rayscale Correction of Underwater Pipeline Images Using Side-Scan Sonar’, IEEE Sensors Journal, 2025 vol. 25, no. 11, pp. 19462-19476.
Wu, Jin.,Wu, S.C, Sun, B.,:’An adaptive methodology for rock mass fracture image enhancement with generalized gamma correction’,Visual Computers., 2023, vol. 40, Iss. 8, pp.1-17.
A.H. Alsaeedi, S.H. Hadi and Y. Alazzawi, ”Adaptive Gamma and Color Correction for Enhancing Low-Light Images”, International journal of intelligent
Engineering and Systems, vol.17, no.4, pp.188- 201, 2024.
L. Zhang and X. Mou, ”FSIM: a feature similarity index for image quality assessment”,IEEE Trans. Image Process., vol.20, no.8, pp. 2378–2386, 2011.
Z. Yang, K. Ma, K. Wang, G. Zhai and W. Zhang, ”Perceptual quality assessment of multi-exposure images”, IEEE Transactions on Image Processing, vol.24, no.11, pp. 3358–3369, 2015.
L. Zhang and A. C. Bovik, ”A feature-enriched completely blind image quality evaluator”, IEEE Transactions on Image Processing, vol.24, no.8, pp.2579–2591, 2015.
L. Zhang, Y. Shen and H. Li, ”VSI: a visual saliencyinduced index for perceptual image quality assessment”, IEEE Trans. Image Process., vol.23, no.10, pp. 4270–4281, 2014.
M.Y. Liu and T. Wang, ”Visual saliency detection based on multiscale deep features”, in 2013, Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), 2013, pp. 5455- 5463.
Y. Fang, K. Ma, Z. Wang, W. Lin and Z. Fang, ”Noreference quality assessment of contrast-distorted images based on natural scene statistics”, IEEE Signal Processing Letters, vol.22, no.7, pp.838–842. 2015.
W. Xue, L. Zhang, X. Mou, ”Gradient magnitude similarity deviation:a highly efficient perceptual image quality index”,IEEE Trans. Image Process., vol.23, no.2, pp. 684–695, 2014.
K. Gu, G. Zhai, W. Lin and M. Zhang, ”Noreference image sharpness assessment in auto regressive parameter space”, IEEE Transactions
on Image Processing, vol.24, no.10, pp.3218–3231, 2015.
F. Liu, W. Liu and Z. Li, ”An improved perceptual image quality assessment model based on GMSD”,IEEE Access, vol.8, pp.166151–166161, 2020.
Y. Rong, J. Zhou, M. Wang and N. Wang, ”A New Super-Resolution Method for Brightness Temperature Images based on SAR Feature Fusion”, in 2025 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Taiyuan, China, 2025, pp. 262-265.
