Dual Recursive Network for Fast Image Deraining

Abstract

Recent years have witnessed the great progress on deep image deraining networks. On the one hand, deraining performance has been significantly improved by designing complex network architectures, yielding high computational cost. On the other hand, several lightweight networks try to improve computational efficiency, but at the cost of notable degrading deraining performance. In this paper, we propose a dual recursive network (DRN) for fast image deraining as well as comparable or superior deraining performance compared with state-of-the-art approaches. Specifically, our DRN utilizes a residual network (ResNet) with only 2 residual blocks (ResBlock), which is recursively unfolded to remove rain streaks in multiple stages. Meanwhile, the 2 ResBlocks can be recursively computed in one stage, forming the dual recursive network. Experimental results show that DRN is very computationally efficient and can achieve favorable deraining results on both synthetic and real rainy images. The source codes and pre-trained models are available at https://github.com/csdwren/DRN.