Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior

TL;DR

This paper introduces a novel stereo deraining network that leverages semantic prior and multi-view information to improve rain removal, outperforming existing methods on both monocular and stereo datasets.

Contribution

It proposes a new stereo deraining framework utilizing semantic-aware modules and fusion networks, advancing rain removal by integrating semantic and multi-view cues.

Findings

Achieves state-of-the-art results on stereo rainy datasets.

Effectively fuses semantic and multi-view information for better deraining.

Introduces new stereo rainy datasets for benchmarking.

Abstract

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.