NAOC Open IR
Image Desaturation for SDO/AIA Using Deep Learning
Yu, Xuexin1,2; Xu, Long1,3; Yan, Yihua1
2021-03-01
Source PublicationSOLAR PHYSICS
ISSN0038-0938
Volume296Issue:3Pages:14
AbstractThe Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) (launched in February 2010) provides uninterrupted full-disk solar images over 10 wavebands. In the case of violent solar flares, saturation would happen to SDO/AIA images in their core regions, which leads to signal loss, hindering us to understand physical mechanism behind solar flares. This paper introduces a deep learning based image restoration model which can recover signal of saturation region by referring to other normal/valid region within an image. The proposed model, namely PCGAN, combines partial convolution (PC) and conditional generative adversarial network (GAN). The PC module was originally designed for image inpainting, for repairing images with scratches and holes. In addition, a new comprehensive loss function consists of an adversarial loss, a pixel reconstruction loss, a gradient loss, a perceptual loss, a style loss and a total variation loss. Moreover, for validating the proposed model, a new dataset consisting of paired saturated and normal SDO/AIA images is established. Experimental results demonstrate that the proposed PCGAN can get appealing desaturated solar images with respect to both objective and subjective validations.
KeywordSolar flare Deep learning Generative adversarial network Saturation Desaturation
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; CAAI-Huawei MindSpore Open Fund ; CAAI-Huawei MindSpore Open Fund ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; CAAI-Huawei MindSpore Open Fund ; CAAI-Huawei MindSpore Open Fund ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; CAAI-Huawei MindSpore Open Fund ; CAAI-Huawei MindSpore Open Fund ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; CAAI-Huawei MindSpore Open Fund ; CAAI-Huawei MindSpore Open Fund
DOI10.1007/s11207-021-01808-2
Language英语
Funding ProjectNational Natural Science Foundation of China[11790301] ; National Natural Science Foundation of China[11790305] ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China[SKLMCC2020KF004] ; CAAI-Huawei MindSpore Open Fund
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; CAAI-Huawei MindSpore Open Fund ; CAAI-Huawei MindSpore Open Fund ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; CAAI-Huawei MindSpore Open Fund ; CAAI-Huawei MindSpore Open Fund ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; CAAI-Huawei MindSpore Open Fund ; CAAI-Huawei MindSpore Open Fund ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; State Key Laboratory of Media Convergence and Communication, Communication University of China, China ; CAAI-Huawei MindSpore Open Fund ; CAAI-Huawei MindSpore Open Fund
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000633379100001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/78734
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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Peng Cheng Lab, Shenzhen 518000, Peoples R China
Recommended Citation
GB/T 7714
Yu, Xuexin,Xu, Long,Yan, Yihua. Image Desaturation for SDO/AIA Using Deep Learning[J]. SOLAR PHYSICS,2021,296(3):14.
APA Yu, Xuexin,Xu, Long,&Yan, Yihua.(2021).Image Desaturation for SDO/AIA Using Deep Learning.SOLAR PHYSICS,296(3),14.
MLA Yu, Xuexin,et al."Image Desaturation for SDO/AIA Using Deep Learning".SOLAR PHYSICS 296.3(2021):14.
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