Non-local channel aggregation network for single image rain removal
文献类型:期刊论文
作者 | Su, Zhipeng1; Zhang YX(张贻雄)1; Zhang, Xiao-Ping3; Qi F(祁峰)2 |
刊名 | Neurocomputing |
出版日期 | 2022 |
卷号 | 469页码:261-272 |
ISSN号 | 0925-2312 |
关键词 | Non-local channel aggregation Rain removal Neural network SIRR problem |
产权排序 | 3 |
英文摘要 | Rain streaks showing in images or videos would severely degrade the performance of computer vision applications. Thus, it is of vital importance to remove rain streaks and facilitate our vision systems. While recent convolutional neural network based methods have shown promising results in single image rain removal (SIRR), they fail to effectively capture long-range location dependencies or aggregate convolutional channel information simultaneously. However, as SIRR is a highly ill-posed problem, these spatial and channel information are very important clues to solve SIRR. First, spatial information could help our model to understand the image context by gathering long-range dependency location information hidden in the image. Second, aggregating channels could help our model to concentrate on channels more related to image background instead of rain streaks. In this paper, we propose a non-local channel aggregation network (NCANet) to address the SIRR problem. NCANet models 2D rainy images as sequences of vectors in three directions, namely vertical direction, transverse direction, and channel direction. Recurrently aggregating information from all three directions enables our model to capture the long-range dependencies in both channels and spatial locations. Extensive experiments on both heavy and light rain image data sets demonstrate the effectiveness of the proposed NCANet model. |
资助项目 | Science and Technology Key Project of Fujian Province[2019H6001] ; Science and Technology Key Project of Fujian Province[2019HZ020009] ; Science and Technology Key Project of Fujian Province[2020HZ020005] ; Science and Technology Key Project of Fujian Province[2021HZ021004] ; Science and Technology Key Project of Fujian Province[2021H61010115] ; National Natural Science Foundation of China[U1705263] ; President's Fund of Xiamen University for Under-graduate[20720212006] ; Open Project of Key Labora-tory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, CAS |
WOS研究方向 | Computer Science |
语种 | 英语 |
WOS记录号 | WOS:000719323600008 |
资助机构 | Science and Technology Key Project of Fujian Province (2019H6001, 2019HZ020009, 2020HZ020005, 2021HZ021004 and 2021H61010115) ; National Natural Science Foundation of China (Grant No. U1705263) ; President’s Fund of Xiamen University for Undergraduate (No. 20720212006) ; Open Project of Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, CAS |
源URL | [http://ir.sia.cn/handle/173321/29888] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Zhang YX(张贻雄) |
作者单位 | 1.Department of Information Science and Engineering, Xiamen University, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, China 3.Department of Electrical Computer and Biomedical Engineering, Ryerson University, Toronto, ON, Canada |
推荐引用方式 GB/T 7714 | Su, Zhipeng,Zhang YX,Zhang, Xiao-Ping,et al. Non-local channel aggregation network for single image rain removal[J]. Neurocomputing,2022,469:261-272. |
APA | Su, Zhipeng,Zhang YX,Zhang, Xiao-Ping,&Qi F.(2022).Non-local channel aggregation network for single image rain removal.Neurocomputing,469,261-272. |
MLA | Su, Zhipeng,et al."Non-local channel aggregation network for single image rain removal".Neurocomputing 469(2022):261-272. |
入库方式: OAI收割
来源:沈阳自动化研究所
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