A deraining with detail-recovery network via context aggregation

As one representative object of bad weather, rain streaks can have a bad influence on image capture. Worse still, computer vision tasks can be badly influenced when working in rainy scenes. Therefore, it makes sense to conduct research on rain removal. This paper introduces a new deraining network (DDRNet). Specifically, rain streaks contain both frequency and spatial information, so we propose a rain removal module (RRM) consisting of a rain channel attention module (RCAM) and a rain spatial attention module (RSAM) for extracting rain information. Furthermore, we design a detail-recovery module (DRM) to extract the background feature so that the original background image details deleted by mistake during the deraining process can be made up by it. Moreover, a two-branch aggregation (TBA) mechanism is adopted to promote the process of information flow that effectively enhances the execution of our DDRNet. The evaluation of our DDRNet on various benchmark datasets has shown the superiority of our algorithm. In addition, a range of low-level experiments, such as object detection and semantic segmentation, provide further evidence of the effectiveness of the proposed DDRNet.