液晶与显示2025，Vol.40Issue(5)：751-760,10.DOI:10.37188/CJLCD.2024-0277
上下文感知的低光图像增强算法
Context aware low-light image enhancement algorithm
摘要
Abstract
Aiming at the problem of low brightness and blurred detail information in low-light images,this paper proposes a context-aware low-light image enhancement algorithm.First,the context-aware module for extracting detail information and edge artifacts was investigated.Nonlinear mapping was performed using activation functions to get the importance of features in the current context.Second,the model used linear attention gating mechanism instead of the multi-head attention module in Transformer.It reduced the computational complexity in high-resolution images while maintaining the performance.Finally,the reconstruction guidance module was designed to focus on the information in the low-light region during image reconstruction.The correlation information between the positions in the input sequence was captured to improve the expressiveness of the model for the reconstruction processing task.The results show that compared with the existing typical low-light enhancement algorithm URetinex,the PSNR and SSIM of images generated on the dataset LOL are increased by 1.33%and 3.73%,and the PSNR and SSIM of images generated on the dataset SICE are increased by 1.2%and 2.8%.The proposed algorithm can effectively enhance low-light images and generate clear and high-fidelity images.关键词
低光增强/Transformer/线性注意力/门控机制Key words
low light enhancement/transformer/linear attention/gating mechanism分类
计算机与自动化引用本文复制引用
张建强,何秋生..上下文感知的低光图像增强算法[J].液晶与显示,2025,40(5):751-760,10.基金项目
国家自然科学基金(No.52274160) （No.52274160）
山西省人民政府资助的中国煤基低碳项目(No.U1510115) Supported by National Natural Science Foundation of China(No.52274160) （No.U1510115）
China's Coal-based Low-carbon Projects Funded by Shanxi Provincial People's Government(No.U1510115) （No.U1510115）
