Low-illumination Image Enhancement Method Based on Retinex and Gamma Transformation

被引：0
|
作者：
Wang, Wenyun [1 ]
Shu, Chenyang [1 ]
Zhu, Longtao [1 ]
Hang, Jinglong [1 ]
Yang, Jingyun [1 ]
Li, Shouke [2 ]
机构：
[1] Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan
[2] College of Civil Engineering, Hunan University, Changsha
来源：
Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences | 2024年 / 51卷 / 10期
关键词：
BM3D algorithm; Gabor filtering; image enhancement; improved Retinex algorithm; low-illumination images;
D O I：
10.16339/j.cnki.hdxbzkb.2024207
中图分类号：
学科分类号：
摘要：
An improved Retinex low-illumination image enhancement algorithm is proposed for the balanced enhancement of low-illumination images while retaining their more detailed information. The algorithm is based on the HSV（Hue，Saturation，Value）color space and enhances the separated luminance and saturation components. First，the brightness component is optimized using Contrast Limited Adaptive Histogram Equalization（CLAHE）to make the image closer to the uniformly illuminated scene，and the saturation component is corrected using Adaptive Gamma. Then，a Block-matching and 3D Filtering （BM3D） algorithm is used to estimate the illumination component，the corresponding reflection component is obtained，and an improved Gamma transform function is proposed to enhance the luminance component based on the information of the illumination component. Meanwhile，the Gabor filter and Canny algorithm are used to extract the details of the original image，and a detail enhancement strategy is proposed to enhance the reflection component and its texture details. Finally，the components are fused with multiple weights，and then the enhanced image is transformed back to RGB space. Experimental results show that the proposed algorithm has better enhancement effect and universality than automatic color equalization，adaptive local tone mapping，low-illumination image enhancement，and multi-scale Retinex with color restoration. After enhancement，the original image showed significant improvements in information entropy，peak signal-to noise ratio，structural similarity index，universal image quality index，average gradient，while the root mean square error decreased significantly. © 2024 Hunan University. All rights reserved.
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页码：136 / 144
页数：8
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