Illumination prior guided low-light image enhancement method

Aiming at the shortcomings of existing low-light image enhancement methods, such as poor illumination adaptability and difficulty in balancing global structure and local details, this paper proposes a light-prior driven low-light enhancement network (LPD-Net). First, an illumination prior module (IPM) is constructed based on Retinex theory, which generates illumination-normalized features by introducing homomorphic filtering priors to achieve global correction of non-uniform illumination. Second, a Mamba enhanced module (MEM) based on an encoder-decoder architecture is designed, employing a hierarchical modeling strategy and constructing a multi-scale pathway consisting of local window Mamba and global Mamba to achieve local detail enhancement and global brightness restoration. On this basis, a dual-stream fusion color modulation (DFCM) module is introduced, which captures horizontal and vertical spatial dependencies through bidirectional convolutional blocks and integrates global-local features using a lightweight attention mechanism. Simultaneously, channel gating and color modulation mechanisms are applied to adaptively restore color information and fine image details. Finally, total variation regularization is introduced to constrain the illumination map, preventing structural inconsistency caused by over-enhancement. Experimental results show that the proposed method achieves 24.370 dB PSNR and 0.853 SSIM on the LOLv1 dataset, significantly outperforming existing methods. The method demonstrates notable advantages in illumination correction, noise suppression, and detail preservation, while also exhibiting strong generalization capability for real-world low-light scenarios.