Multi-Scale Retinex Unfolding Network for Low-Light Image Enhancement
Huake Wang, Xingsong Hou, Junxuan Li, Yadi Yan, Wenke Sun, Xin Zeng, Kaibing Zhang, Xiangyong Cao
Abstract
Retinex theory-based low-light image enhancement methods have received increasing attention and achieved tremendous advancements. However, there still exist two seldom-explored issues: 1) The above methods only formally simulate the Retinex decomposition, resulting in lacking explicit interpretability. 2) They usually are performed in single-scale space, leading to suboptimal enhancement results. In this paper, we propose an interpretable Multi-scale Retinex Unfolding Network (MRUNet) for low-light image enhancement, which can tackle both of the aforementioned issues simultaneously. Specifically, we formulate low-light image enhancement as a multi-scale Retinex optimization problem and design an iteration minimization solution to solve it. The optimization solution is further unfolded to fabricate MRUNet, which is empowered with clear physical significance and multi-scale prior knowledge in favor of image enhancement. However, it will aggravate model size and efficiency when exploiting multiple proximal mapping networks to extract multi-scale prior from multi-scale inputs. To surmount the issue, we propose a Scale-Aware Proximal mapping Module (SAPM), which efficiently collect multi-scale prior knowledge via the weight sharing strategy. In SAPM, we tailor a scale-aware transformer to model the specific scale-similarity among different scales. Extensive experiments manifest that MRUNet surpasses other Retinex-based low-light image enhancement methods on multiple benchmarks.