DiffLight: Integrating Content and Detail for Low-light Image Enhancement

Yixu Feng, Shuo Hou, Haotian Lin, Yu Zhu, Peng Wu, Wei Dong, Jinqiu Sun, Qingsen Yan, Yanning Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6143-6152

The Low Light Image Enhancement (LLIE) task has been a hotspot in low-level computer vision research. The camera sensor can only capture a small amount of ambient light signal in low-light condition resulting in significant noise black pseudo artifacts in images which not only degrade visual quality but also affect the performance of downstream visual tasks. However current methods often produce overly smoothed and distorted results or introduce strong noise artifacts. Moreover for recent UHD high-definition low-light images due to GPU memory limitations LLIE must be conducted in patches leading to block artifacts. Faced with these challenges we propose a dual-branch pipeline called DiffLight. Specifically it consists of the Denoising Enhancement (DE) branch and the Detail Preservation (DP) branch. The DE-branch adopts a combination of DiffIR and LEDNet to reduce noise and enhance brightness while the DP-branch utilizes a novel Light Full-Former (LFF) method which comprises 20 Full-Attention (LFA) modules to preserve full-scale image details. To tackle block artifacts we further introduce Progressive Patch Fusion (PPF) for patch fusion. Experimental results demonstrate that our approach is high-ranked in the CVPR2024 NTIRE Low Light Enhancement challenge and produced state-of-the (SOTA) results on other datasets.