UG-Prior: Uncertainty-Guided Priors with Online Refinement for Zero-Shot Low-Light Image Enhancement

Pixel-level and distribution-level supervision for low-light image enhancement (LLIE) remain sensitive to scene and degradation in the training data, limiting generalization. Physical illumination-invariant priors bridge low/normal-light domains, posing a promising direction for improved generalization. However, their unreliable estimation from degraded observations hampers cross-domain robustness and faithful enhancement. To alleviate this issue, we propose UG-Prior, an Uncertainty-Guided zero-shot LLIE framework. Specifically, we propose uncertainty-guided multi-scale fusion to construct robust and informative priors with reliable structural and chromatic cues. An auxiliary decoder is proposed to regularize generative features by reconstructing clean intrinsic representations. During inference, its predictions enable online prior-image co-optimization, which refines the conditioning prior as the image evolves. Extensive experiments demonstrate that our method achieves state-of-the-art zero-shot LLIE. Relative to the recent SOTA, ZeroIDIR, our UG-Prior improves PSNR by 0.42 dB and LPIPS by 0.02 on LOL, with strong generalization across diverse scenes and illumination conditions.