Single image rain removal using recurrent scale-guide networks

Abstract

Recently, removing rain streaks from a single image has attracted a lot of attention because rain streaks can severely degrade the perceptual quality of the image and cause many practical vision systems to fail. Single image deraining can be served as a pre-processing step to improve the performance of high-level vision tasks such as object detection and video surveillance. In this paper, we propose recurrent scale-guide networks for single image deraining. Although the multi-scale strategy has been successfully applied to many computer vision problems, the correlation between different scales has not been explored in most existing methods. To overcome this deficiency, we propose two types of scale-guide blocks and develop two combinations between the blocks. One type of scale-guide block is that small scale guides the large, and the other is that large scale guides the small. Moreover, we extend the single-stage deraining model to the multi-stage recurrent framework and introduce the Long Short-Term Memory (LSTM) to link every stage. Extensive experiments verify that the scale-guide manner boosts the deraining performance and the recurrent style improves the deraining results. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deraining methods on three widely used datasets: Rain100H, Rain100L, and Rain1200. The source codes can be found athttps://supercong94.wixsite.com/supercong94.