Learning Structural Priors via Laplacian RWKV Diffusion with Light-Effect Dataset for Nighttime Visibility Enhancement

Images captured in low-light nighttime scenes suffer from light effects. Existing nighttime visibility enhancement methods predominantly focus on low-light image enhancement (LLIE), neglecting light-effect suppression (LES). Current LES methods mainly rely on unsupervised or zero-shot learning due to the lack of paired nighttime light-effect datasets. We construct a large-scale nighttime dataset containing diverse light effects to enable supervised learning for joint LES and LLIE. We design a two-stage structural prior-guided diffusion model for nighttime visibility enhancement, proposing a Laplacian decomposition physical model and a dual-loop Receptance Weighted Key Value (RWKV) to separate light effects from structural features. Experimental results demonstrate that our method outperforms state-of-the-art (SOTA) methods in LLIE and LES tasks. Through supervised training on our dataset, our method achieves optimal performance in joint LES and LLIE while maintaining effectiveness across various real-world scenarios.