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Video Desnowing and Deraining Based on Matrix Decomposition
Ren WH(任卫红); Tian JD(田建东); Han Z(韩志); Chan,Antoni; Tang YD(唐延东)
作者部门机器人学研究室
会议名称30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)
会议日期July 21-26, 2017
会议地点Honolulu, USA
会议录名称30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)
出版者IEEE
出版地New York
2017
页码2838-2847
收录类别EI ; CPCI(ISTP)
EI收录号20181304952985
WOS记录号WOS:000418371402095
产权排序1
ISSN号1063-6919
ISBN号978-1-5386-0457-1
摘要The existing snow/rain removal methods often fail for heavy snow/rain and dynamic scene. One reason for the failure is due to the assumption that all the snowflakes/rain streaks are sparse in snow/rain scenes. The other is that the existing methods often can not differentiate moving objects and snowflakes/rain streaks. In this paper, we propose a model based on matrix decomposition for video desnowing and deraining to solve the problems mentioned above. We divide snowflakes/rain streaks into two categories: sparse ones and dense ones. With background fluctuations and optical flow information, the detection of moving objects and sparse snowflakes/rain streaks is formulated as a multi-label Markov Random Fields (MRFs). As for dense snowflakes/rain streaks, they are considered to obey Gaussian distribution. The snowflakes/rain streaks, including sparse ones and dense ones, in scene backgrounds are removed by low-rank representation of the backgrounds. Meanwhile, a group sparsity term in our model is designed to filter snow/rain pixels within the moving objects. Experimental results show that our proposed model performs better than the state-of-the-art methods for snow and rain removal.
语种英语
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21359
专题机器人学研究室
通讯作者Tian JD(田建东)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.City University of Hong Kong
推荐引用方式
GB/T 7714
Ren WH,Tian JD,Han Z,et al. Video Desnowing and Deraining Based on Matrix Decomposition[C]. New York:IEEE,2017:2838-2847.
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