Maximum-Value retinex decomposition guided generative priors for joint deraining and low-light image enhancement
Yanfei Sun, J. -T. WANG, Rui Yin · Information Sciences · 2026
Nighttime rainy conditions severely degrade visual quality in applications such as autonomous driving and aerial surveillance, where images suffer from compounded low-light and rain degradations. Diffusion models offer strong generative priors but face limitations in image restoration, including poor controllability, structural distortion, and domain gaps with degraded images. We present MR-SDformer, a novel framework that integrates Retinex-based decomposition with diffusion priors for joint nighttime deraining and low-light enhancement. The key innovation is the Maximum-Value Retinex decomposition, which isolates high-intensity rain streaks into the illumination map and produces a rain-free reflectance map that faithfully preserves intrinsic scene content. This decomposition not only bridges the gap between rainy inputs and rain-free priors but also provides complementary guidance to the generative process. Building on this, we design an asymmetric Hybrid Conditional Transformer that leverages the decomposed illumination and reflectance maps to condition the frozen diffusion model more effectively, enabling precise multi-modal feature fusion and high-fidelity reconstruction. Extensive experiments on both synthetic and real-world datasets confirm that MR-SDformer achieves state-of-the-art performance, delivering clearer structure, enhanced illumination, and more realistic visual quality under nighttime rainy conditions. • Maximum-Value Retinex decouples rain and low-light for robust restoration. • Bridges domain gap to unlock frozen diffusion priors for multi-degradations. • Retinex-based attention injects physical priors into the diffusion process. • Achieves SOTA performance on both synthetic and real-world datasets.