Degradation-aware Dynamic Adaptation for Unpaired Low-Light Image Enhancement

Pretrained diffusion models provide powerful natural-image priors for low-light image enhancement (LLIE), offering a promising way to reduce dependence on paired training data. However, existing unpaired diffusion-based methods typically employ a fixed conditioning mechanism shared by all inputs. This is suboptimal for real-world LLIE, where different images may exhibit substantially different combinations of illumination deficiency, color distortion, and structural degradation. To address this limitation, we propose Degradation-aware Dynamic Adaptation (DDA), a one-step unpaired LLIE framework that adaptively exploits pretrained diffusion priors according to the degradation characteristics of each input. DDA contains two complementary modules. The Degradation-aware Prior Generation (DPG) module analyzes the illumination, color, and structural degradation of the input and produces an image-specific representation that identifies its restoration demands. The Degradation-Driven State-Glimpse (DDSG) adapter then translates these demands into layer-compatible dynamic updates for the image-conditioning pathway, allowing the diffusion prior to be injected differently for different low-light images. Extensive experiments on LOL-v2-real and LSRW, together with downstream classification, detection, and semantic segmentation evaluations, demonstrate that DDA consistently outperforms existing unsupervised LLIE methods. Code will be released upon acceptance.