Reusing Learned Degradation Models via Counter-Degradation Guidance for Diffusion Dehazing

Recent diffusion-based dehazing methods increasingly rely on learned haze generators to produce realistic training data for real-world restoration. However, once the restoration model has been trained, the degradation model is typically discarded, despite retaining explicit knowledge of how haze affects the diffusion prediction. We revisit this unused knowledge and propose Haze Counter-Degradation Guidance (HazeCDG), a training-free framework that reuses a pretrained haze generator to improve an already trained diffusion restorer at inference time. HazeCDG compares the pretrained haze generator with its frozen backbone at the same diffusion state, so that their prediction difference reveals how the learned haze process locally changes the diffusion prediction. It then constructs a counter-degradation reference by reversing this learned degradation change and guides the restoration prediction toward it while preserving the relative prediction offset between the restoration and degradation models. Across real-world haze benchmarks, HazeCDG substantially reduces residual haze while preserving the perceptual strengths of the original restorer, bringing DiffDehaze into the performance range of recent dedicated dehazing methods. Importantly, these gains are obtained while keeping both the degradation and restoration models frozen; HazeCDG requires no retraining, test-time parameter adaptation, or manually tuned guidance scale. Our results demonstrate that degradation models learned for data synthesis can remain valuable after training as complementary sources of inference-time knowledge for diffusion restoration. Our code is available at https://anonymous.4open.science/r/HAZECDG-E2C1/README.md.