MPC-Net: Multi-Prior Collaborative Network for Low-Light Image Enhancement

Chunyan She, Fujun Han, Lidan Wang, Shukai Duan, Tingwen Huang

Abstract: Low-light image enhancement aims to obtain a normal-light image by adjusting the illumination of a low-light image. The existing methods do not fully explore the prior information hidden in low-light images, which raises the problems of detail loss and color distortion. To alleviate these issues, we propose a multi-prior collaborative network (MPC-Net) with transformer for low-light image enhancement. It extracts the indispensable prior information to facilitate high-quality image enhancement. Specifically, a pre-trained high-level vision model is employed to extract coarse texture and structure, which is then refined through a proposed self-distillation module to obtain compact representation for texture and structure. Furthermore, we design a color branch consisting of negative residual blocks and a pyramid structure to solve for noise-free color prior, aiming to provide the enhancer with a modeling mechanism for color information. Finally, a transformer-based multi-prior fusion module is developed to aggregate the content and prior information. Extensive experiments show that the proposed MPC-Net achieves superior performance on three referenced datasets and four no-referenced datasets. Our code is available at: https://github.com/Shecyy/MPC-Net.