PhaseNet: A Deep Learning Based Phase Reconstruction Method for Ground-Based Astronomy-Reference-Cited by-同舟云学术

PhaseNet: A Deep Learning Based Phase Reconstruction Method for Ground-Based Astronomy

Published:2024-07-15 Issue:3 Volume:17 Page:1511-1538
ISSN:1936-4954
Container-title:SIAM Journal on Imaging Sciences
language:en
Short-container-title:SIAM J. Imaging Sci.

Author:

Zheng Dihan¹^ORCID,Tang Shiqi²^ORCID,Wagner Roland³,Ramlau Ronny⁴,Bao Chenglong⁵^ORCID,Chan Raymond H.⁶^ORCID

Affiliation:

1. Yau Mathematical Sciences Center, Tsinghua University, Beijing, China.

2. Department of Mathematics, City University of Hong Kong, Kowloon Tong, Hong Kong.

3. Industrial Mathematics Institute, Johannes Kepler University, 4040 Linz, Austria.

4. Industrial Mathematics Institute, Johannes Kepler University, 4040 Linz, Austria, and Johann Radon Institute for Computational and Applied Mathematics, 4040 Linz, Austria.

5. Yau Mathematical Sciences Center, Tsinghua University, Beijing, China, and Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, China.

6. Department of Mathematics, City University of Hong Kong, Kowloon Tong, Hong Kong, and Hong Kong Center for Cerebro-Cardiovascular Health Engineering, Hong Kong Science Park, Hong Kong.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Austrian Science Fund

Hong Kong Research Grant Council

CRF

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference47 articles.

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5. Point-spread function reconstruction in ground-based astronomy by l^1-l^p model