Illumination-Guided Grouped Attention and Masked Progressive Denoising for Low-Light Image Enhancement

Existing Transformer- or Mamba-based low-light image enhancement (LLIE) methods can capture long-range dependencies in images and achieve global degradation restoration, but suffer from insufficient detail recovery. Besides, these methods do not consider the different illumination levels in various regions or enhance the regional illumination accordingly. Furthermore, existing denoising methods often overfit to single noise distribution and type, showing poor generalization to the noises in low-light images. To address these issues, we propose an Illumination-guided Grouped Attention (IGA) module and a Masked Progressive Denoising (MPD) module for low-light image enhancement. The IGA module first improves the local feature representation through detail recovery operations, and then iteratively groups the image representation based on the illumination levels and conducts grouped attention to achieve both fine detail recovery and adaptive regional illumination optimization. The MPD module explores correlations within and among regions to enhance the texture and structure representations from a local to global perspective, thus achieving both local and global denoising and well denoising generalization capability. Extensive quantitative and qualitative experiments on eight datasets demonstrate that our proposed method outperforms the existing state-of-the-art low-light image enhancement methods. Furthermore, plugging our proposed IGA and MPD modules into existing Transformer- or Mamba-based LLIE methods can significantly improve their performance, further demonstrating the modules' ability to address the common issues in such methods.