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题名Real-time video dehazing via incremental transmission learning and spatial-temporally coherent regularization
作者
发表日期2021-10-11
发表期刊Neurocomputing
ISSN/eISSN0925-2312
卷号458页码:602-614
摘要

Video dehazing is of crucial importance to the outdoor surveillance, and its main challenges mainly come from the spatial-temporal coherence and computational efficiency. This paper presents an efficient real-time video dehazing approach via incremental transmission learning and spatial-temporally coherent regularization, while explicitly suppressing the possible visual artifacts. First, we propose to initialize the transmission map frame-by-frame by a boundary constrained open dark channel model. Then, supposing a scene point yields highly correlated transmission values between adjacent frames, we impose a temporally coherent term to maintain the temporal consistency of consecutive transmission values, and simultaneously derive an incremental transmission adjusting term to adapt the abrupt scene depth changes between the adjacent frames. Accordingly, the highly correlated and spatial-temporally coherent transmission maps can be optimized for each video frame dehazing, whereby the flickering artifacts can be well reduced. Finally, we group each haze/haze-free images in pair and further utilize the guided joint bilateral filter to suppress the visual artifacts that possibly amplified during the dehazing process. The experimental results show that the proposed video dehazing approach is able to well preserve the spatial-temporal coherence, runs sufficiently fast, and also performs favorably compared to the state-of-the-art methods.

关键词Boundary constrained dark channel model Coherent regularization Incremental transmission learning Video dehazing
DOI10.1016/j.neucom.2020.02.134
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收录类别SCIE
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000689715100013
Scopus入藏号2-s2.0-85097056105
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/13026
专题个人在本单位外知识产出
理工科技学院
作者单位
1.Department of Computer Science,Huaqiao University,Xiamen,China
2.State Key Laboratory of Integrated Services Networks,Xidian University,Xi'an,China
3.Fujian Key Laboratory of Big Data Intelligence and Security,Huaqiao University,Xiamen,China
推荐引用方式
GB/T 7714
Peng, Shujuan,Zhang, He,Liu, Xinet al. Real-time video dehazing via incremental transmission learning and spatial-temporally coherent regularization[J]. Neurocomputing, 2021, 458: 602-614.
APA Peng, Shujuan, Zhang, He, Liu, Xin, Fan, Wentao, Zhong, Bineng, & Du, Jixiang. (2021). Real-time video dehazing via incremental transmission learning and spatial-temporally coherent regularization. Neurocomputing, 458, 602-614.
MLA Peng, Shujuan,et al."Real-time video dehazing via incremental transmission learning and spatial-temporally coherent regularization". Neurocomputing 458(2021): 602-614.
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