  Recent diffusion models have exhibited great potential in generative modeling
tasks. Part of their success can be attributed to the ability of training
stable on huge sets of paired synthetic data. However, adapting these models to
real-world image deraining remains difficult for two aspects. First, collecting
a large-scale paired real-world clean/rainy dataset is unavailable while
regular conditional diffusion models heavily rely on paired data for training.
Second, real-world rain usually reflects real-world scenarios with a variety of
unknown rain degradation types, which poses a significant challenge for the
generative modeling process. To meet these challenges, we propose RainDiff, the
first real-world image deraining paradigm based on diffusion models, serving as
a new standard bar for real-world image deraining. We address the first
challenge by introducing a stable and non-adversarial unpaired cycle-consistent
architecture that can be trained, end-to-end, with only unpaired data for
supervision; and the second challenge by proposing a degradation-conditioned
diffusion model that refines the desired output via a diffusive generative
process conditioned by learned priors of multiple rain degradations. Extensive
experiments confirm the superiority of our RainDiff over existing
unpaired/semi-supervised methods and show its competitive advantages over
several fully-supervised ones.
