Single Image Deraining Using Bilateral Recurrent Network

Abstract

Single image deraining has received considerable progress based on deep convolutional neural network (CNN). In existing deep deraining methods, CNNs are deployed to extract rain streaks while failing in learning direct mapping from rainy image to clean background image, and their architectures become more and more complicated. In this work, we first propose a single recurrent network (SRN) by recursively unfolding a shallow residual network, where a recurrent layer is adopted to propagate deep features across multiple stages. This simple SRN is effective not only in learning residual mapping for extracting rain streaks, but also in learning direct mapping for predicting clean background image. Furthermore, two SRNs are coupled to simultaneously exploit rain streak layer and clean background image layer. Instead of naive combination, we propose bilateral LSTMs, which not only can respectively propagate deep features of rain streak layer and background image layer across stages, but also bring the interplay between these two SRNs, finally forming bilateral recurrent network (BRN). The experimental results demonstrate that our BRN notably outperforms state-of-the-art deep deraining networks on synthetic datasets quantitatively and qualitatively. The proposed methods also perform more favorably in terms of generalization performance on real-world rainy dataset. All the source code and pre-trained models are available at https://github.com/csdwren/RecDerain.