Color and Luminance Separated Enhancement for Low-Light Images with Brightness Guidance

被引:1
作者
Zhang, Feng [1 ]
Liu, Xinran [1 ]
Gao, Changxin [1 ]
Sang, Nong [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
关键词
low-light image enhancement; diffusion models; Retinex decomposition strategy; brightness guidance; RETINEX; NETWORK;
D O I
10.3390/s24092711
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Existing retinex-based low-light image enhancement strategies focus heavily on crafting complex networks for Retinex decomposition but often result in imprecise estimations. To overcome the limitations of previous methods, we introduce a straightforward yet effective strategy for Retinex decomposition, dividing images into colormaps and graymaps as new estimations for reflectance and illumination maps. The enhancement of these maps is separately conducted using a diffusion model for improved restoration. Furthermore, we address the dual challenge of perturbation removal and brightness adjustment in illumination maps by incorporating brightness guidance. This guidance aids in precisely adjusting the brightness while eliminating disturbances, ensuring a more effective enhancement process. Extensive quantitative and qualitative experimental analyses demonstrate that our proposed method improves the performance by approximately 4.4% on the LOL dataset compared to other state-of-the-art diffusion-based methods, while also validating the model's generalizability across multiple real-world datasets.
引用
收藏
页数:19
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