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

Paper:

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

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

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:
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Last updated on Dec. 01, 2022