  Existing unpaired image deraining approaches face challenges in accurately
capture the distinguishing characteristics between the rainy and clean domains,
resulting in residual degradation and color distortion within the reconstructed
images. To this end, we propose an energy-informed diffusion model for unpaired
photo-realistic image deraining (UPID-EDM). Initially, we delve into the
intricate visual-language priors embedded within the contrastive language-image
pre-training model (CLIP), and demonstrate that the CLIP priors aid in the
discrimination of rainy and clean images. Furthermore, we introduce a
dual-consistent energy function (DEF) that retains the rain-irrelevant
characteristics while eliminating the rain-relevant features. This energy
function is trained by the non-corresponding rainy and clean images. In
addition, we employ the rain-relevance discarding energy function (RDEF) and
the rain-irrelevance preserving energy function (RPEF) to direct the reverse
sampling procedure of a pre-trained diffusion model, effectively removing the
rain streaks while preserving the image contents. Extensive experiments
demonstrate that our energy-informed model surpasses the existing unpaired
learning approaches in terms of both supervised and no-reference metrics.
