Distilling Both Clarity and Rain: Equivariant Self-Collaborative Distillation for Unpaired Image Deraining

Unpaired image deraining methods usually jointly learn image restoration and realistic rain synthesis from independently collected rainy and clean images, leaving degradation modeling weakly constrained. We propose Equivariant Self-Collaborative Distillation (ESCD), which converts the restoration ability of a frozen derainer into complementary clarity, rain, and objective knowledge. Given a rainy image and an equivariant view, the teacher produces aligned clean predictions and dual rain residuals. The clean predictions distill clarity into the student, while the residuals provide transferable rain structure. A Structure and Appearance Factorized Rain Generator separates clean content, rain appearance, and rain structure, then uses the fused residual representation to gate affine modulation. Rain-Responsive Collaborative Regularization further converts the residual pair into bounded spatial weights for synthesis fidelity and channel consistency, emphasizing rainy regions without discarding background supervision. Iterative Self-Collaborative Distillation promotes the refined student to the next frozen teacher only at cycle boundaries and carries forward the complete collaborative system, which progressively evolves knowledge under stable supervision within each cycle. Extensive experiments on paired synthetic, paired real, and unpaired real datasets demonstrate state of the art performance among existing unpaired deraining approaches, with consistent improvements in full reference fidelity and perceptual image quality as well as strong generalization to real rain.