Image restoration with point-spread function regularization and active learning

doi:10.1093/mnras/stad3363

YNAO OpenIR > 天文技术实验室

	Image restoration with point-spread function regularization and active learning
	Jia, Peng 1,2,3; Lv, Jiameng 1; Ning, Runyu 1; Song, Yu 1; Li, Nan 4; Ji KF(季凯帆)5 ; Cui, Chenzhou 3; Li, Shanshan 3
发表期刊	MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
	2024-01
卷号	527 期号:3 页码:6581-6590
DOI	10.1093/mnras/stad3363
产权排序	第5完成单位
收录类别	SCI ; EI
关键词	methods: numerical techniques: image processing software: data analysis
摘要	Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.
资助项目	National Natural Science Foundation of China (NSFC)[12173027]; National Natural Science Foundation of China (NSFC)[12173062]; China Manned Space Project[CMS-CSST-2021-A01]; Square Kilometer Array (SKA) Project[2020SKA0110102]; Civil Aerospace Technology Research Project[D050105]; Major Key Project of PCL; Shanxi Graduate Innovation Project[2022Y274]; Alfred P. Sloan Foundation; National Science Foundation; US Department of Energy; National Aeronautics and Space Administration; Japanese Monbukagakusho; Max Planck Society; Higher Education Funding Council for England; American Museum of Natural History; Astrophysical Institute Potsdam; University of Basel; University of Cambridge; Case Western Reserve University; University of Chicago; Drexel University; Fermilab; Institute for Advanced Study; Japan Participation Group; Johns Hopkins University; Joint Institute for Nuclear Astrophysics; Kavli Institute for Particle Astrophysics and Cosmology; Korean Scientist Group; Chinese Academy of Sciences (LAMOST); Los Alamos National Laboratory; Max-Planck-Institute for Astronomy (MPIA); Max-Planck-Institute for Astrophysics (MPA); New Mexico State University; Ohio State University; University of Pittsburgh; University of Portsmouth; Princeton University; United States Naval Observatory; University of Washington
项目资助者	National Natural Science Foundation of China (NSFC)[12173027, 12173062] ; China Manned Space Project[CMS-CSST-2021-A01] ; Square Kilometer Array (SKA) Project[2020SKA0110102] ; Civil Aerospace Technology Research Project[D050105] ; Major Key Project of PCL ; Shanxi Graduate Innovation Project[2022Y274] ; Alfred P. Sloan Foundation ; National Science Foundation ; US Department of Energy ; National Aeronautics and Space Administration ; Japanese Monbukagakusho ; Max Planck Society ; Higher Education Funding Council for England ; American Museum of Natural History ; Astrophysical Institute Potsdam ; University of Basel ; University of Cambridge ; Case Western Reserve University ; University of Chicago ; Drexel University ; Fermilab ; Institute for Advanced Study ; Japan Participation Group ; Johns Hopkins University ; Joint Institute for Nuclear Astrophysics ; Kavli Institute for Particle Astrophysics and Cosmology ; Korean Scientist Group ; Chinese Academy of Sciences (LAMOST) ; Los Alamos National Laboratory ; Max-Planck-Institute for Astronomy (MPIA) ; Max-Planck-Institute for Astrophysics (MPA) ; New Mexico State University ; Ohio State University ; University of Pittsburgh ; University of Portsmouth ; Princeton University ; United States Naval Observatory ; University of Washington
语种	英语
学科领域	天文学
文章类型	Article
出版者	OXFORD UNIV PRESS
出版地	GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
ISSN	0035-8711
URL	查看原文
WOS记录号	WOS:001131511000041
WOS研究方向	Astronomy & Astrophysics
WOS类目	Astronomy & Astrophysics
关键词[WOS]	SURFACE BRIGHTNESS GALAXIES ; DIGITAL SKY SURVEY ; DECONVOLUTION ; MODEL ; NET
EI入藏号	20235115255876
EI主题词	Efficiency
EI分类号	461.4 Ergonomics and Human Factors Engineering - 741.1 Light/Optics - 913.1 Production Engineering - 921.6 Numerical Methods
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
版本	出版稿
条目标识符	http://ir.ynao.ac.cn/handle/114a53/26531
专题	天文技术实验室
作者单位	1.College of Electronic Information and Optical Engineering, Taiyuan 030024, China; 2.Peng Cheng Lab, Shenzhen 518066, China; 3.Department of Physics, Durham University, Durham DH1 3LE, UK; 4.National Astronomical Observatories, Beijing 100101, China; 5.Yunnan Observatories, Kunming, Yunnan, China
推荐引用方式 GB/T 7714	Jia, Peng,Lv, Jiameng,Ning, Runyu,et al. Image restoration with point-spread function regularization and active learning[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2024,527(3):6581-6590.
APA	Jia, Peng.,Lv, Jiameng.,Ning, Runyu.,Song, Yu.,Li, Nan.,...&Li, Shanshan.(2024).Image restoration with point-spread function regularization and active learning.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,527(3),6581-6590.
MLA	Jia, Peng,et al."Image restoration with point-spread function regularization and active learning".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 527.3(2024):6581-6590.

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