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Multi-scale Optimal Fusion model for single image dehazing
Zhao, Dong1,2; Xu, Long1; Yan, Yihua1; Chen, Jie3; Duan, Ling-Yu3,4
刊名SIGNAL PROCESSING-IMAGE COMMUNICATION
2019-05-01
卷号74页码:253-265
关键词Single image dehazing Multi-scale dehazing Dark channel prior Multi-scale Optimal Fusion
ISSN号0923-5965
DOI10.1016/j.image.2019.02.004
英文摘要Image acquisition is usually vulnerable to bad weathers, like haze, fog and smoke. Haze removal, namely dehazing has always been a great challenge in many fields. This paper proposes an efficient and fast dehazing algorithm for addressing transmission map misestimation and oversaturation commonly happening in dehazing. We discover that the transmission map is commonly misestimated around the edges where grayscale change abruptly. These Transmission MisEstimated (TME) edges further result in halo artifacts in patch-wise dehazing. Although pixel-wise method is free from halo artifacts, it has trouble with oversaturation. Therefore, we firstly propose a TME recognition method to distinguish TME and non-TME regions, Secondly, we propose a Multi-scale Optimal Fusion (MOF) model to fuse pixel-wise and patch-wise transmission maps optimally to avoid misestimated transmission region. This MOF is then embedded into patch-wise dehazing to suppress halo artifacts. Furthermore, we provide two post-processing methods to improve robustness and reduce computational complexity of the MOF. Extensive experimental results demonstrate that, the MOF can achieve additional improvement beyond the prototypes of the benchmarks; in addition, the MOF embedded dehazing algorithm outperforms most of the state-of-the-arts in single image dehazing. For implementation details, source code can be accessed via https://github.com/phoenixtreesky7/mof_dehazing.
资助项目National Natural Science Foundation of China (NSFC)[61572461] ; National Natural Science Foundation of China (NSFC)[6166114605] ; National Natural Science Foundation of China (NSFC)[U1611461] ; National Natural Science Foundation of China (NSFC)[11433006] ; National Natural Science Foundation of China (NSFC)[11790301] ; National Natural Science Foundation of China (NSFC)[11790305] ; PKU-NTU Joint Research Institute (JRI) ; CAS "100-Talents"
WOS关键词HAZE REMOVAL ; FRAMEWORK
WOS研究方向Engineering
语种英语
出版者ELSEVIER SCIENCE BV
WOS记录号WOS:000465366200023
资助机构National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; PKU-NTU Joint Research Institute (JRI) ; PKU-NTU Joint Research Institute (JRI) ; CAS "100-Talents" ; CAS "100-Talents" ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; PKU-NTU Joint Research Institute (JRI) ; PKU-NTU Joint Research Institute (JRI) ; CAS "100-Talents" ; CAS "100-Talents" ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; PKU-NTU Joint Research Institute (JRI) ; PKU-NTU Joint Research Institute (JRI) ; CAS "100-Talents" ; CAS "100-Talents" ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; PKU-NTU Joint Research Institute (JRI) ; PKU-NTU Joint Research Institute (JRI) ; CAS "100-Talents" ; CAS "100-Talents"
内容类型期刊论文
源URL[http://ir.bao.ac.cn/handle/114a11/26304]  
专题中国科学院国家天文台
通讯作者Xu, Long
作者单位1.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Peking Univ, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China
4.Peng Cheng Lab, Shenzhen, Peoples R China
推荐引用方式
GB/T 7714
Zhao, Dong,Xu, Long,Yan, Yihua,et al. Multi-scale Optimal Fusion model for single image dehazing[J]. SIGNAL PROCESSING-IMAGE COMMUNICATION,2019,74:253-265.
APA Zhao, Dong,Xu, Long,Yan, Yihua,Chen, Jie,&Duan, Ling-Yu.(2019).Multi-scale Optimal Fusion model for single image dehazing.SIGNAL PROCESSING-IMAGE COMMUNICATION,74,253-265.
MLA Zhao, Dong,et al."Multi-scale Optimal Fusion model for single image dehazing".SIGNAL PROCESSING-IMAGE COMMUNICATION 74(2019):253-265.
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