Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding

Date

2020

Authors

Zhang, Kaihao
Luo, Wenhan
Ren, Wenqi
Wang, Jingwen
Zhao, Fang
Ma, Lin
Li, Hongdong

Journal Title

Journal ISSN

Volume Title

Publisher

Springer Nature Switzerland AG

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. 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.

Description

Keywords

Stereo deraining, Semantic understanding, Rethinking loop, View fusion, Deep learning

Citation

Source

Computer Vision – ECCV 2020

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

10.1007/978-3-030-58583-9_5

Restricted until

2099-12-31