Context-detail-aware United Network for Single Image Deraining

Abstract

Images captured outdoors are often affected by rainy days, resulting in a severe deterioration in the visual quality of the captured images and a decrease in the performance of related applications. Therefore, single image deraining has attracted attention as a challenging research topic. Nowadays, there are two common deraining architectures in single image deraining. The first one is to restore the rain-free image by deducting rain streaks learned by the model from the rain image, but the background structure is easily mistaken for rain streaks and subtracted. The other one is to directly learn the clean background structure through the model using rain images, but it is difficult to completely remove the rain streaks due to the complexity of the information in images with rain. Therefore, current methods cannot balance rain streak removal and rain-free image background restoration in a single architecture and achieve good results. To address this issue, we propose a novel framework, namely, Context-Detail-Aware United Network (CDaUNet), which combines the above two architectures in this study. More specifically, we divide the restoration of the background structure of rain-free images and the learning of rain streaks into two independent sub-networks. The proposed Structure-Aware Rain Removal Network (SaRRN) is to learn the background structure in images to reconstruct clean rain-free images, whereas Detail-Aware Rain Streak Learning Network (DaRLN) is proposed to learn the details of rain streaks in images. Finally, we fuse the results generated by the two sub-networks through our designed Dual Architecture Fusion Network (DAFN) to reconstruct original rain images to effectively fuse the results of the two sub-networks. The experimental results show that CDaUNet achieves satisfactory performance in comparison with the state-of-the-art approaches included in rain streak removal and rain-free image structure restoration architectures on both synthetic and real image datasets, confirming the effectiveness of our method.