Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior
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 exploi...
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| Published in: | International journal of computer vision Vol. 130; no. 7; pp. 1754 - 1769 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer US
01.07.2022
Springer Springer Nature B.V |
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| ISSN: | 0920-5691, 1573-1405 |
| Online Access: | Get full text |
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| 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.
https://github.com/HDCVLab/Stereo-Image-Deraining
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| AbstractList | 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. 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. https://github.com/HDCVLab/Stereo-Image-Deraining. 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. https://github.com/HDCVLab/Stereo-Image-Deraining . |
| Audience | Academic |
| Author | Yu, Yanjiang Li, Changsheng Liu, Wei Ren, Wenqi Ma, Lin Zhang, Kaihao Luo, Wenhan Zhao, Fang Li, Hongdong |
| Author_xml | – sequence: 1 givenname: Kaihao surname: Zhang fullname: Zhang, Kaihao organization: Australian National University – sequence: 2 givenname: Wenhan orcidid: 0000-0002-5697-4168 surname: Luo fullname: Luo, Wenhan email: whluo.china@gmail.com organization: Sun Yat-sen University – sequence: 3 givenname: Yanjiang surname: Yu fullname: Yu, Yanjiang organization: Beijing Institute of Technology – sequence: 4 givenname: Wenqi surname: Ren fullname: Ren, Wenqi organization: Sun Yat-sen University – sequence: 5 givenname: Fang surname: Zhao fullname: Zhao, Fang organization: Inception Institute of Artificial Intelligence – sequence: 6 givenname: Changsheng surname: Li fullname: Li, Changsheng organization: Beijing Institute of Technology – sequence: 7 givenname: Lin surname: Ma fullname: Ma, Lin organization: Meituan Group – sequence: 8 givenname: Wei surname: Liu fullname: Liu, Wei organization: Tencent – sequence: 9 givenname: Hongdong surname: Li fullname: Li, Hongdong organization: Australian National University |
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| Keywords | Deep learning Parallel stereo network Stereo image deraining View fusion |
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| SubjectTerms | Algorithms Artificial Intelligence Computer Imaging Computer Science Computer vision Datasets Feature extraction Image Processing and Computer Vision Image quality Image segmentation Machine vision Pattern Recognition Pattern Recognition and Graphics Rain Semantic segmentation Semantics Vision Vision systems |
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| Title | Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior |
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