Multi-branch low-light enhancement algorithm based on spatial transformation

被引：0

作者：

Wang W. ^{[1
]}

Sun Y. ^{[1
]}

Zou C. ^{[3
]}

Tang D. ^{[2
]}

Fang Z. ^{[6
]}

Tao B. ^{[4
,5
]}

机构：

[1] Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan

[2] School of Computer Information Management, Inner Mongolia University of Finance and Economics, Hohhot

[3] College of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan

[4] Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan

[5] Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan

[6] Hubei Key Laboratory of Hydroelectric Design & Maintenance, China Three Gorges University, Yichang

来源：

Multimedia Tools and Applications | 2025年 / 84卷 / 18期

基金：

中国国家自然科学基金;

关键词：

Guided image filtering; Image enhancement; Machine learning; Retinex; Space conversion;

D O I：

10.1007/s11042-024-19743-2

中图分类号：

学科分类号：

摘要：

Many state-of-the-art low-light image enhancement techniques now suffer from issues like color distortion, detail blurring, and the halo effect, hindering their ability to produce visual effects. This paper presents a multi-branch low-light enhancement algorithm based on spatial transformation to significantly improve the visual impact of images under low-light settings, overcoming the limitations of existing approaches. The process involves dividing the initial low-light picture into three distinct layers. Bootstrap filtering and Contrast Limited Adaptive Histogram Equalization are used to process the main feature layer. Primary feature extraction is used to extract features for the compensation layer, which is then integrated with Contrast Limited Adaptive Histogram Equalization and MSRCR. The auxiliary layer utilizes spatial transformation to apply gamma correction to the V-channel and performs color space transformation and additional processing. Ultimately, these three layers are combined and refined to create the final improved image. Based on the experimental results, the proposed method outperforms the standard MSRCR algorithm by 52.47%, 71.58%, and 12.10%, respectively, on the assessment metrics AG, SD, and IE. LOE declines by 22.3%. The processed image presents a good visual quality, effectively enhancing the image brightness while retaining a large amount of information and improving the image details. This paper proposes an effective method to deal with the problem of low illumination image enhancement, which provides a strong reference for future research and application in related fields. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

引用

页码：19647 / 19667

页数：20

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