JACIII Vol.26 No.6 pp. 875-883
doi: 10.20965/jaciii.2022.p0875


Contrast Enhancement Method Based on Multi-Scale Retinex and Adaptive Gamma Correction

Shaoying Ma, Chuanying Yang, and Shi Bao

College of Information Engineering, Inner Mongolia University of Technology
49 Aimin Street, Xincheng District, Hohhot 010051, China

Corresponding author

March 9, 2022
May 31, 2022
November 20, 2022
multi-scale retinex, gamma correction with weighting distribution, lightness, hue saturation value (HSV) color space

The most common methods to improve the quality of images with insufficient visibility are retinex-based and gamma correction methods. The fundamental assumption of retinex theory is that the color of an object can be represented as the multiplication of its illumination and reflectance. The retinex-based method improves the quality of the insufficiently visible image by repairing its illumination. The multi-scale retinex (MSR) is a classic retinex-based method. Though MSR better enhances the details of the image, it sometimes reverses its lightness value. The method based on adaptive gamma correction with weighting distribution (AGCWD) is to modify the visibility of images by gamma function. However, AGCWD provides a good enhancement effect on low-contrast areas, it also enhances the high-light region making it too bright. In this paper, a method that combines the advantages of MSR and AGCWD is proposed. Firstly, the advatages of MSR and AGCWD are preserved into detailed image through the weight that considers illumination. Then, the image constructed by combining the detailed and original images could maintain the contrast of the high-light region and enhance details of the low-light region. The validity of the proposed method is shown by experiments using several images.

Cite this article as:
S. Ma, C. Yang, and S. Bao, “Contrast Enhancement Method Based on Multi-Scale Retinex and Adaptive Gamma Correction,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.6, pp. 875-883, 2022.
Data files:
  1. [1] A. K. Jain, “Fundamentals of Digital Image Processing,” Prentice-Hall, 1989.
  2. [2] S. M. Pizer et al., “Adaptive histogram equalization and its variations,” Comput. Vis., Graph., Image Process., Vol.39, No.3, pp. 355-368, 1987.
  3. [3] Y.-T. Kim, “Contrast enhancement using brightness preserving bihistogram equalization,” IEEE Trans. Consum. Electron., Vol.43, No.1, pp. 1-8, 1997.
  4. [4] Y. Wang, Q. Chen, and B. Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method,” IEEE Trans. Consum. Electron., Vol.45, No.1, pp. 68-75, 1999.
  5. [5] S.-D. Chen and A. R. Ramli, “Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation,” IEEE Trans. Consum. Electron., Vol.49, No.4, pp. 1301-1309, 2003.
  6. [6] C. Wang and Z. Ye, “Brightness preserving histogram equalization with maximum entropy: A variational perspective,” IEEE Trans. Consum. Electron., Vol.51, No.4, pp. 1326-1334, 2005.
  7. [7] H. Ibrahim and N. S. P. Kong, “Brightness preserving dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consum. Electron., Vol.53, No.4, pp. 1752-1758, 2007.
  8. [8] G.-H. Park, H.-H. Cho, and M.-R. Choi, “A contrast enhancement method using dynamic range separate histogram equalization,” IEEE Trans. Consum. Electron., Vol.54, No.4, pp. 1981-1987, 2008.
  9. [9] T. Arici, S. Dikbas, and Y. Altunbasak, “A histogram modification framework and its application for image contrast enhancement,” IEEE Trans. Image Process., Vol.18, No.9, pp. 1921-1935, 2009.
  10. [10] S.-C. Huang, F.-C. Cheng, and Y.-S. Chiu, “Efficient Contrast Enhancement Using Adaptive Gamma Correction with Weighting Distribution,” IEEE Trans. Image Process., Vol.22, No.3, pp. 1032-1041, 2013.
  11. [11] E. H. Land, “The retinex theory of color vision,” Sci. Amer., Vol.237, No.6, pp. 108-128, 1977.
  12. [12] D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. Image Process., Vol.6, No.7, pp. 965-976, 1997.
  13. [13] R. Kimmel et al., “A variational framework for retinex,” Int. J. of Computer Vision, Vol.52, pp. 7-23, 2003.
  14. [14] I.-S. Jang, K.-H. Park, and Y.-H. Ha, “Modified multi-scaled retinex using chrominanceticity of highlight region for correcting color distortion,” Proc. of 16th Color and Imaging Conf. (CIC2008), pp. 50-55, 2008.
  15. [15] Y. Terai et al., “Color image contrast enhancement by retinex model,” Proc. of 2009 IEEE 13th Int. Symp. on Consumer Electronics, pp. 392-393, 2009.
  16. [16] I.-S. Jang et al., “Adaptive color enhancement based on multi-scaled retinex using local contrast of the input image,” Proc. of 2010 Int. Symp. on Optomechatronic Technologies, doi: 10.1109/ISOT.2010.5687343, 2010.
  17. [17] E. Zhang, H. Yang, and M. Xu, “A novel tone mapping method for high dynamic range image by incorporating edge-preserving filter into method based on retinex,” Applied Mathematics & Information Sciences, Vol.9, No.1, pp. 411-417, 2015.
  18. [18] Y. Ru and G. Tanaka, “Proposal of multiscale retinex using illumination adjustment for digital images,” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E99-A, No.11, pp. 2003-2007, 2016.
  19. [19] Q. Fu, C. Jung, and K. Xu, “Retinex-Based Perceptual Contrast Enhancement in Images Using Luminance Adaptation,” IEEE Access, Vol.6, pp. 61277-61286, 2018.
  20. [20] S. Bao, S. Ma, and C. Yang, “Multi-scale retinex-based contrast enhancement method for preserving the naturalness of color image,” Optical Review, Vol.27, pp. 475-485, 2020.
  21. [21] M. K. Agoston, “Computer Graphics and Geometric Modeling: Implementation and Algorithms,” Springer-Verlag London, 2005.
  22. [22] C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical J., Vol.27, pp. 379-423, 623-656, 1948.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Dec. 01, 2022