Zero-Shot Low-Light Image Enhancement via Diffusion-Learned Photometric Priors

Pre-trained diffusion models encode natural-image statistics that can be exploited as priors for zero-shot low-light image enhancement (LLIE). Many existing diffusion-based LLIE methods, however, directly use the generative process for image reconstruction, coupling photometric correction with content reconstruction. This coupling may lead to structural drift and synthesized details inconsistent with the low-light observation. We instead exploit the complementary information provided by the observation and the diffusion prior, using the former to anchor scene structure and the latter to provide learned statistics of plausible normal-light appearance. Based on this principle, we propose a structure–photometry decoupled framework that restricts diffusion to photometric guidance while deriving image structure from the observation. We first recover a noise-aware structure estimate from the low-light input, while a short reverse trajectory of a frozen Stable Diffusion model produces a stochastic normal-light reference. Rather than using this reference for direct reconstruction, we project its photometric relationship with the structure estimate into global and spatially varying RGB gain–bias transformations. A globally anchored spatial trust region further stabilizes local corrections against unreliable local statistics and sampling-induced variability in the diffusion reference. The resulting transformations are applied to the observation-derived structure, combining input-anchored content with diffusion-learned normal-light statistics. Experiments on paired and unpaired LLIE benchmarks demonstrate leading zero-shot performance, with the best results on most evaluated metrics and efficient inference. Beyond LLIE, the framework achieves competitive performance on auto white balance without task-specific modification, suggesting the broader applicability of diffusion-learned photometric priors.