A low-light image enhancement network based on multi-layer feature aggregation and multi-branch attention mechanisms

Current low-light image enhancement methods often suffer from insufficient detail enhancement, color distortion, excessive brightness enhancement, and limited generalization performance. To improve these issues, this paper proposes a multi-layer feature enhancement expression method utilizing multi-branch attention mechanisms. Specifically, single-level or single-scale feature extraction often fails to capture sufficient local details and global contextual features. Therefore, this article designs a multi-scale feature information extension module to obtain rich multi-scale feature information from different levels. Furthermore, to better capture key feature information across different levels, a multi-branch attention module is designed according to the characteristics of features at different scales. For example, lower network layers contain rich detailed information, for which local attention is used to optimize feature extraction. Higher network layers contain a wide range of contextual features that are processed using global attention. By using this strategy to enhance the feature expression ability of images. Finally, these feature information are fused to form a more accurate image feature representation. Extensive experiments on multiple public datasets demonstrate that the proposed method performs well and can enhance low-light images effectively.