Temporal Rain Decomposition with Spatial Structure Guidance for Video Deraining

Abstract

Recently, removing rain streaks from videos has drawn wide concerns in vision and multimedia communities. But existing works ignore the depicts of image inherent structure and rain location to cause details loss, and their adopted manners of exploiting temporal information are still insufficient. In this work, we propose a multi-frame deraining network with temporal rain decomposition and spatial structure guidance to more effectively accomplish video deraining. A learnable decomposition method is defined to learn the distribution of rain, where the location map acts on a single-frame deraining block. We construct a multi-frame fusion module with a detailed guidance map to integrate temporal and spatial information. Many evaluated experiments demonstrate that our algorithm performs favorably on video deraining tasks compared with other methods. The elaborate ablation study in terms of network architecture fully indicates the effectiveness of our network.