  Rain is a common natural phenomenon. Taking images in the rain however often
results in degraded quality of images, thus compromises the performance of many
computer vision systems. Most existing de-rain algorithms use only one single
input image and aim to recover a clean image. Few work has exploited stereo
images. Moreover, even for single image based monocular deraining, many current
methods fail to complete the task satisfactorily because they mostly rely on
per pixel loss functions and ignore semantic information. In this paper, we
present a Paired Rain Removal Network (PRRNet), which exploits both stereo
images and semantic information. Specifically, we develop a Semantic-Aware
Deraining Module (SADM) which solves both tasks of semantic segmentation and
deraining of scenes, and a Semantic-Fusion Network (SFNet) and a View-Fusion
Network (VFNet) which fuse semantic information and multi-view information
respectively. In addition, we also introduce an Enhanced Paired Rain Removal
Network (EPRRNet) which exploits semantic prior to remove rain streaks from
stereo images. We first use a coarse deraining network to reduce the rain
streaks on the input images, and then adopt a pre-trained semantic segmentation
network to extract semantic features from the coarse derained image. Finally, a
parallel stereo deraining network fuses semantic and multi-view information to
restore finer results. We also propose new stereo based rainy datasets for
benchmarking. Experiments on both monocular and the newly proposed stereo rainy
datasets demonstrate that the proposed method achieves the state-of-the-art
performance.
