Retinexformer+:Retinex-Based Dual-Channel Transformer for Low-Light Image Enhancement  

作　　者：Song Liu Hongying Zhang Xue Li Xi Yang 

机构地区：[1]School of Information Engineering,Southwest University of Science and Technology,Mianyang,621000,China [2]Criminal Investigation Department,Sichuan Police College,Luzhou,646000,China [3]School of Electronics and Information,Mianyang Polytechnic,Mianyang,621000,China

出　　处：《Computers, Materials & Continua》2025年第2期1969-1984,共16页计算机、材料和连续体(英文)

基　　金：supported by the Key Laboratory of Forensic Science and Technology at College of Sichuan Province(2023YB04).

摘　　要：Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels as distance increases. In illumination restoration, we used Unet++ with multi-level skip connections to better integrate semantic information at different scales. The designed Illumination Fusion Dual Self-Attention (IF-DSA) module embeds multi-scale dilated convolutions to achieve spatial self-attention. This module captures long-range spatial semantic relationships within acceptable computational complexity. Experimental results on the Low-Light (LOL) dataset show that Retexformer+ outperforms other State-Of-The-Art (SOTA) methods in both quantitative and qualitative evaluations, with the computational complexity increased to an acceptable 51.63 G FLOPS. On the LOL_v1 dataset, RetinexFormer+ shows an increase of 1.15 in Peak Signal-to-Noise Ratio (PSNR) and a decrease of 0.39 in Root Mean Square Error (RMSE). On the LOL_v2_real dataset, the PSNR increases by 0.42 and the RMSE decreases by 0.18. Experimental results on the Exdark dataset show that Retexformer+ can effectively enhance real-scene images and maintain their semantic information.

关 键 词：Low-light image enhancement RETINEX transformer model 

分 类 号：O17[理学—数学]