Multi-Domain Prior Guided Network for Low-Light Image Enhancement
Most existing low-light image enhancement methods mainly rely on feature modeling in a single domain, making it difficult to simultaneously achieve global illumination correction, local detail restoration, and noise suppression. To overcome this limitation, we propose a low-light image enhancement network(MPNet) that leverages multi-domain prior attention, and introduce MPGSA (Multi-domain Prior Guided Self-Attention) as its core module. Specifically, MPGSA incorporates priors from the spatial, Fourier, and wavelet domains into the attention mechanism. Through complementary multi-domain modeling, it improves global illumination restoration, local texture reconstruction, and noise suppression. Extensive experimental results demonstrate that the proposed method not only achieves outstanding visual quality and competitive quantitative metrics on multiple public datasets, but also performs excellently on downstream tasks, showcasing strong generalization ability and broad application potential.