Refinement for image rain removal based on diffusion model

Image rain removal task has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of outdoor vision tasks. Conventional methods based on model-driven or data-driven have achieved effective performance. However, when the image background is highly similar to the rain steaks, conventional methods cannot effectively identify the background and rain steaks, resulting in excessive removal of background or ineffective rain steaks removal. Inspired by the amazing performance of diffusion models in dealing with image inverse problems, we convert the refinement of those results into an image inpainting task. Specifically, we obtain the rain steaks and the background as two images from the rain degraded image by the conventional rain removal method. Then we process the rain steaks image by setting threshold and binarization, then mask the background with the processed image. Due to the powerful generating ability of diffusion model, our method can repair the excessive removal parts of background while remove the residual rain streaks. The experiment demonstrates that our method achieves superior results in both quantitative evaluation metrics and visual effects compared to current methods.