计算机工程与应用2025，Vol.61Issue(10)：238-246,9.DOI:10.3778/j.issn.1002-8331.2401-0249
基于多级特征融合的低光图像增强网络
Low-Light Image Enhancement Network of Multi-Level Feature Fusion
摘要
Abstract
In recent years,the integration of Retinex theory and CNN for low-light image enhancement has achieved sig nificant progress,the KinD++method stands out particularly in illumination adjustment.However,this method still has shortcomings when dealing with images with uneven or extremely low illumination.In order to address the issues of detail blurring,artifacting,and color distortion,when the KinD++processes images with uneven or extremely low illumi-nation,this paper makes improvements on decomposition network and restoration network,proposes a low-light image enhancement network of multi-level feature fusion.Through cross-level fusion of deep,medium,and shallow features,a more accurate estimation of the reflection is achieved.In the restoration network,De_Block denoising block and Canny edge enhancement block are designed,which can keep the details clear while denoising effectively.A color loss function is used to achieve natural and bright image colors.The experimental results demonstrate that the proposed method can effectively improve image brightness while restoring details and colors,thereby avoiding artifacts.Compared to the original method,on the LOL dataset,PSNR,SSIM,and NIQE are improved by 13%,12%,and 28%respectively.Additionally,supe-rior performance is demonstrated on multiple benchmark datasets such as LIME,VV,MEF,DICM,and LLIV-Phone-imgT.关键词
低光图像增强/非均匀光/极低光照/Retinex理论/多级特征融合/Canny边缘增强Key words
low-light image enhancement/uneven/extremely low illumination/Retinex theory/multi-level feature fusion/Canny edge enhancement分类
信息技术与安全科学引用本文复制引用
牟琦,马悦悦,李洪安,李占利..基于多级特征融合的低光图像增强网络[J].计算机工程与应用,2025,61(10):238-246,9.基金项目
国家重点研发计划(2022YFB3304401). （2022YFB3304401）
