SDNet: A Novel Separable Structure for Single Image Deraining

Abstract

Rain streaks degrade visual quality of images and severely affect the performance of computer vision system. Recently, deep convolutional neural networks (DCNNs) have shown the potential in single image deraining. However, their complex structures with large-scale of parameters lead to strong requirement of large storage, which still limits the applications of deraining systems. In this paper, we propose a separable deraining network (SDNet) which provides a more flexible and simply baseline of deraining network by using a cascaded framework to achieve automatic trade-off between model performance and computational resources. Besides, a novel module called group-atrous spatial pyramid (G-ASP) and a network separation strategy (NSS) are proposed to extract more discriminative multi-scale information with fewer parameters and adapts our model to different levels rain-streaks for reasonable allocation of computational resources automatically. The experimental results on public datasets demonstrate that our SDNet achieves promising performances with much fewer parameters than other methods.