Abstract
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.
Abstract (translated)
在真实低光环境下采集的人脸图像常面临多重退化问题——光照不足、模糊、噪声及低可见度等。现有级联方案常因严重的误差累积而受限，而通用联合模型则缺乏显式的人脸先验知识，难以重建清晰的面部结构。本文提出一种无需训练的物理感知语义扩散模型PASDiff。为获得合理的光照与色彩分布，我们借助逆强度加权与Retinex理论引入光度约束，从而可靠恢复图像可见度与自然色度。为忠实重建面部细节，我们设计的风格无关结构注入模块（SASI）可从现成的人脸先验中提取结构特征，同时过滤其内在光度偏差，使身份特征与物理约束自然融合。此外，我们构建了包含700张复杂退化低光人脸图像的真实世界基准数据集WildDark-Face。大量实验表明，PASDiff显著优于现有方法，在自然光照、色彩恢复与身份一致性之间取得了优异平衡。
URL
https://arxiv.org/abs/2603.24969
