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Image restoration with point-spread function regularization and active learning

文献类型：期刊论文

作者Jia, Peng1,3,5; Lv, Jiameng1; Ning, Runyu1; Song, Yu1; Li, Nan4; Ji KF(季凯帆)2; Cui, Chenzhou3; Li, Shanshan3
刊名MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
出版日期2024-01
卷号527期号:3页码:6581-6590
ISSN号0035-8711
关键词methods: numerical techniques: image processing software: data analysis
DOI10.1093/mnras/stad3363
产权排序第5完成单位
文献子类Article
英文摘要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.
学科主题天文学
URL标识查看原文
出版地GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
WOS关键词SURFACE BRIGHTNESS GALAXIES ; DIGITAL SKY SURVEY ; DECONVOLUTION ; MODEL ; NET
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
WOS研究方向Astronomy & Astrophysics
语种英语
出版者OXFORD UNIV PRESS
WOS记录号WOS:001131511000041
资助机构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
版本出版稿
源URL[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.Yunnan Observatories, Kunming, Yunnan, China
3.Department of Physics, Durham University, Durham DH1 3LE, UK;
4.National Astronomical Observatories, Beijing 100101, China;
5.Peng Cheng Lab, Shenzhen 518066, 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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来源：云南天文台

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