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Solar image deconvolution by generative adversarial network
Xu, Long1; Sun, Wen-Qing1; Yan, Yi-Hua1; Zhang, Wei-Qiang2
2020-11-01
Source PublicationRESEARCH IN ASTRONOMY AND ASTROPHYSICS
ISSN1674-4527
Volume20Issue:11Pages:9
AbstractWith aperture synthesis (AS) technique, a number of small antennas can be assembled to form a large telescope whose spatial resolution is determined by the distance of two farthest antennas instead of the diameter of a single-dish antenna. In contrast from a direct imaging system, an AS telescope captures the Fourier coefficients of a spatial object, and then implement inverse Fourier transform to reconstruct the spatial image. Due to the limited number of antennas, the Fourier coefficients are extremely sparse in practice, resulting in a very blurry image. To remove/reduce blur, "CLEAN" deconvolution has been widely used in the literature. However, it was initially designed for a point source. For an extended source, like the Sun, its efficiency is unsatisfactory. In this study, a deep neural network, referring to Generative Adversarial Network (GAN), is proposed for solar image deconvolution. The experimental results demonstrate that the proposed model is markedly better than traditional CLEAN on solar images. The main purpose of this work is visual inspection instead of quantitative scientific computation. We believe that this will also help scientists to better understand solar phenomena with high quality images.
Keyworddeep learning (DL) generative adversarial network (GAN) solar radio astronomy image reconstruction aperture synthesis
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC)
DOI10.1088/1674-4527/20/11/170
WOS KeywordQUALITY ASSESSMENT ; SPARSE ; SYSTEM
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[61572461] ; National Natural Science Foundation of China (NSFC)[61811530282] ; National Natural Science Foundation of China (NSFC)[61872429] ; National Natural Science Foundation of China (NSFC)[11790301] ; National Natural Science Foundation of China (NSFC)[11790305]
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC)
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000593518600001
PublisherNATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
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Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/80571
Collection中国科学院国家天文台
Corresponding AuthorXu, Long
Affiliation1.Chinese Acad Sci, Key Lab Solar Act, Natl Astron Observ, Beijing 100101, Peoples R China
2.Shenzhen Univ, Coll Math & Stat, Shenzhen 518060, Peoples R China
Recommended Citation
GB/T 7714
Xu, Long,Sun, Wen-Qing,Yan, Yi-Hua,et al. Solar image deconvolution by generative adversarial network[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2020,20(11):9.
APA Xu, Long,Sun, Wen-Qing,Yan, Yi-Hua,&Zhang, Wei-Qiang.(2020).Solar image deconvolution by generative adversarial network.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,20(11),9.
MLA Xu, Long,et al."Solar image deconvolution by generative adversarial network".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 20.11(2020):9.
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