Learning Structural Illumination for Unsupervised Low-light Enhancement
Existing unsupervised low-light enhancement methods struggle to disentangle spatially varying relative illumination structure from absolute exposure levels and are often compromised by low signal-to-noise ratio regions; moreover, their reliance on fixed exposure targets limits adaptability across diverse scenes. This work proposes RISE, a novel framework that explicitly decouples relative illumination structure and absolute exposure for the first time, and generates dual photometric exposure references directly from the input image to enable scene-adaptive unsupervised enhancement. By integrating reliable bright-region-guided illumination estimation with spatial propagation modeling, RISE achieves state-of-the-art performance across multiple benchmarks and real-world scenarios, significantly improving interpretability, robustness, cross-scene generalization, and producing visually natural enhanced images.