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 |
DOI | 10.1016/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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