Tian Ma, Chenhui Fu*, Jiayi Yang, Jiehui Zhang, Chuyang Shang
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1103-1122, 2023, DOI:10.32604/cmc.2023.042416
Abstract Low-light image enhancement methods have limitations in addressing issues such as color distortion, lack of vibrancy, and uneven light distribution and often require paired training data. To address these issues, we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network (RF-Net), which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms. This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images. In the first stage, we design a… More >
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