Recurrent Network Knowledge Distillation for Image Rain Removal

Abstract

Single-image rain removal (SIRR) based on deep learning has long been a problem of great interest in low-level vision systems. However, traditional convolutional neural network (CNN)-based approaches fail to capture long-range location dependencies effectively and may cause the image background blurred. In this article, we propose a knowledge distilling deraining network (KDRN) to address the SIRR problem. In the proposed network, the teacher regards rain streaks as a linear combination of many residual networks. It is used for image reconstruction at different resolutions. With the aid of a teacher network, the proposed deraining network performs better. A spatial channel aggregation residual attention block (SCARAB) is designed to remove the rain streaks. The block not only concentrates on the rain streak features but also captures the spatial-channel information of the image. For the network structure, we used an end-to-end approach to design the teacher and student networks separately. The proposed KDRN obtains the predicted residual image by a combination of the stage-wise results and the original input image. Extensive experiments show that the proposed KDRN obtains better subjective quality than most of the compared methods, on both heavy and light rain data sets.