Mar 26, 2026 · cs.CVJ/K move · Enter open · S save
Yilin Ni, Wenjie Li, Zhengxue Wang, Juncheng Li+2
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China · School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China · PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China · School of Computer Science and Technology, East China Normal University, Shanghai, China
Face images captured in real-world low light suffer multiple degradations-low illumination, blur, noise, and low visibility, etc. Existing cascaded solutions often suffer from severe error accumulation, while generic joint models lack explicit facial priors and struggle to resolve clear face structures. In this paper, we propose PASDiff, a Physics-Aware Semantic Diffusion with a training-free manner. To achieve a plausible illumination and color distribution, we leverage inverse intensity weighting and Retinex theory to introduce photometric constraints, thereby reliably recovering visibility and natural chromaticity. To faithfully reconstruct facial details, our Style-Agnostic Structural Injection (SASI) extracts structures from an off-the-shelf facial prior while filtering out its intrinsic photometric biases, seamlessly harmonizing identity features with physical constraints. Furthermore, we construct WildDark-Face, a real-world benchmark of 700 low-light facial images with complex degradations. Extensive experiments demonstrate that PASDiff significantly outperforms existing methods, achieving a superior balance among natural illumination, color recovery, and identity consistency. Code and dataset will be available at https://github.com/IVIPLab/PASDiff.