Single Image Deraining using a Recurrent Multi-scale Aggregation and Enhancement Network

Single image deraining is an ill-posed inverse problem due to the presence of non-uniform rain shapes, directions, and densities in images. In this paper, we propose a novel progressive single image deraining method named Recurrent Multi-scale Aggregation and Enhancement Network (ReMAEN). Differing from previous methods, ReMAEN contains a symmetric structure where recurrent blocks with shared channel attention are applied to select useful information collaboratively and remove rain streaks stage by stage. In ReMAEN, a Multi-scale Aggregation and Enhancement Block (MAEB) is constructed to detect multi-scale rain details. Moreover, to better leverage the rain details from rainy images, ReMAEN enables a symmetric skipping connection from low level to high level. Extensive experiments on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art methods tremendously. The source code is available at https://github.com/nnUyi/ReMAEN.