光：科学与应用（英文版）2024，Vol.13Issue(2)：190-235,46.DOI:10.1038/s41377-023-01340-x
On the use of deep learning for phase recovery
On the use of deep learning for phase recovery
Kaiqiang Wang 1Jianlin Zhao 2Edmund Y.Lam 3Li Song 3Chutian Wang 3Zhenbo Ren 2Guangyuan Zhao 4Jiazhen Dou 5Jianglei Di 5George Barbastathis 6Renjie Zhou4
作者信息
- 1. Department of Electrical and Electronic Engineering,The University of Hong Kong,Hong Kong SAR,China||School of Physical Science and Technology,Northwestern Polytechnical University,Xi'an,China||Department of Biomedical Engineering,The Chinese University of Hong Kong,Hong Kong SAR,China
- 2. School of Physical Science and Technology,Northwestern Polytechnical University,Xi'an,China
- 3. Department of Electrical and Electronic Engineering,The University of Hong Kong,Hong Kong SAR,China
- 4. Department of Biomedical Engineering,The Chinese University of Hong Kong,Hong Kong SAR,China
- 5. School of Information Engineering,Guangdong University of Technology,Guangzhou,China
- 6. Department of Mechanical Engineering,Massachusetts Institute of Technology,Cambridge,MA,USA
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Kaiqiang Wang,Jianlin Zhao,Edmund Y.Lam,Li Song,Chutian Wang,Zhenbo Ren,Guangyuan Zhao,Jiazhen Dou,Jianglei Di,George Barbastathis,Renjie Zhou..On the use of deep learning for phase recovery[J].光：科学与应用（英文版）,2024,13(2):190-235,46.基金项目
The work was supported in part by the National Natural Science Foundation of China(61927810),the Research Grants Council of Hong Kong(GRF 17201620,GRF 17200321,RIF R7003-21)and the Hong Kong Innovation and Technology Fund(ITS/148/20).We thank Yi Zhang and Heng Du in CUHK for proofreading. （61927810）
