Low‐Light Image Enhancement Algorithm Based on Improved <scp>MSRCP</scp> With Chromaticity Preservation

ABSTRACT In response to the issues of poor sharpness and low information entropy in traditional MSRCP (Multi‐Scale Retinex with Color Restoration) algorithms for image enhancement, we propose an improved MSRCP algorithm for low‐light image enhancement with chromaticity preservation. First, we replaced the extrema calculation method in the color restoration function with a calculation method based on clipped pixel ratios. Then, we combined guided filtering and Gaussian filtering to calculate the incident component. Finally, we conducted experiments using six different low‐light images and compared the results with the traditional MSRCP algorithm, such as SSR, MSR, MSRCR, and MSRCP. The experimental results show that our method improved the sharpness and information entropy values in the five comparison images by 5.6%–35.6% and 0.18%–15.3%, respectively.

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Publication Details

Journal
Concurrency and Computation Practice and Experience
Published
2025-02-10
DOI
https://doi.org/10.1002/cpe.8396
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Low‐Light Image Enhancement Algorithm Based on Improved MSRCP With Chromaticity Preservation

Chunmiao Wei, Weijiang Feng, Xinhui Jiang, Zhijin Wang et al.
Concurrency and Computation Practice and Experience
Image Enhancement Techniques
article

Low‐Light Image Enhancement Algorithm Based on Improved MSRCP With Chromaticity Preservation

Chunmiao Wei, Weijiang Feng, Xinhui Jiang, Zhijin Wang, Yuhang Wang, J. S. Huang
article en

Abstract

ABSTRACT In response to the issues of poor sharpness and low information entropy in traditional MSRCP (Multi‐Scale Retinex with Color Restoration) algorithms for image enhancement, we propose an improved MSRCP algorithm for low‐light image enhancement with chromaticity preservation. First, we replaced the extrema calculation method in the color restoration function with a calculation method based on clipped pixel ratios. Then, we combined guided filtering and Gaussian filtering to calculate the incident component. Finally, we conducted experiments using six different low‐light images and compared the results with the traditional MSRCP algorithm, such as SSR, MSR, MSRCR, and MSRCP. The experimental results show that our method improved the sharpness and information entropy values in the five comparison images by 5.6%–35.6% and 0.18%–15.3%, respectively.

Concurrency and Computation Practice and ExperienceVol. 37(4-5)
Guangxi University of Science and Technology (CN), Zhengzhou Railway Vocational & Technical College (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 98%
Image Enhancement Techniques
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