深圳大学学报（理工版）2025，Vol.42Issue(3)：326-333,8.DOI:10.3724/SP.J.1249.2025.03326
基于全局鲁棒Retinex模型的多通道弱光图像增强
Multi-channel low-light image enhancement method based on global robust Retinex model
摘要
Abstract
In response to the issues where existing illumination enhancement methods ignore the changes in hue and saturation under different lighting conditions,resulting in the loss of excessive detail information while suppressing noise,we propose a global robust Retinex model(GRM)based on the Retinex theory to enhance illumination across all color channels.A contraction mapping is used to implement fractional-order norms,making the decomposed illumination map smoother.For the reflection map gradient,we employ a weight matrix and regularization term calculated based on the input image information to obtain a reflection map with less noise and more details.The alternating direction method of multipliers is chosen to solve the model,and gamma correction is applied to obtain the ideal illumination map.The processed reflection map is then combined with the illumination map to yield the final result.Experimental results on the Adobe FiveK,DICM,VV,and LIME datasets show that GRM not only competes with methods such as the robust Retinex model,low-rank regularization Retinex model,and plug-and-play Retinex model in terms of peak signal-to-noise ratio,structural similarity,and image quality assessment metrics,but also demonstrates stronger denoising capabilities and more significant image lighting recovery.The GRM model provides a superior solution for enhancing noisy images captured in low-light environments.关键词
计算机图像/弱光图像增强/Retinex模型/交替方向乘子法/分数阶范数/图像降噪Key words
computer image/low-light image enhancement/Retinex model/alternating direction method of multipliers/fractional-order norm/image denoising分类
信息技术与安全科学引用本文复制引用
吴翰宁,易积政,李潇瑶,李思佳..基于全局鲁棒Retinex模型的多通道弱光图像增强[J].深圳大学学报（理工版）,2025,42(3):326-333,8.基金项目
Hunan Provincial Graduate Research Innovation Project(CX20240692) （CX20240692）
Graduate Research Innovation Fund Project of Central South University of Forestry and Technology(2023CX02091) 湖南省研究生科研创新资助项目(CX20240692) （2023CX02091）
中南林业科技大学研究生科研创新基金资助项目(2023CX02091) （2023CX02091）
