On the use of deep learning for phase recovery
Abstract Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of a...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2024-01-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-023-01340-x |
| _version_ | 1827388147781074944 |
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| author | Kaiqiang Wang Li Song Chutian Wang Zhenbo Ren Guangyuan Zhao Jiazhen Dou Jianglei Di George Barbastathis Renjie Zhou Jianlin Zhao Edmund Y. Lam |
| author_facet | Kaiqiang Wang Li Song Chutian Wang Zhenbo Ren Guangyuan Zhao Jiazhen Dou Jianglei Di George Barbastathis Renjie Zhou Jianlin Zhao Edmund Y. Lam |
| author_sort | Kaiqiang Wang |
| collection | DOAJ |
| description | Abstract Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR. |
| first_indexed | 2024-03-08T16:13:29Z |
| format | Article |
| id | doaj.art-902fb0bd38584acfb1ef0d00334cd63b |
| institution | Directory Open Access Journal |
| issn | 2047-7538 |
| language | English |
| last_indexed | 2024-03-08T16:13:29Z |
| publishDate | 2024-01-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj.art-902fb0bd38584acfb1ef0d00334cd63b2024-01-07T12:47:47ZengNature Publishing GroupLight: Science & Applications2047-75382024-01-0113114610.1038/s41377-023-01340-xOn the use of deep learning for phase recoveryKaiqiang Wang0Li Song1Chutian Wang2Zhenbo Ren3Guangyuan Zhao4Jiazhen Dou5Jianglei Di6George Barbastathis7Renjie Zhou8Jianlin Zhao9Edmund Y. Lam10Department of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongDepartment of Electrical and Electronic Engineering, The University of Hong KongSchool of Physical Science and Technology, Northwestern Polytechnical UniversityDepartment of Biomedical Engineering, The Chinese University of Hong KongSchool of Information Engineering, Guangdong University of TechnologySchool of Information Engineering, Guangdong University of TechnologyDepartment of Mechanical Engineering, Massachusetts Institute of TechnologyDepartment of Biomedical Engineering, The Chinese University of Hong KongSchool of Physical Science and Technology, Northwestern Polytechnical UniversityDepartment of Electrical and Electronic Engineering, The University of Hong KongAbstract Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.https://doi.org/10.1038/s41377-023-01340-x |
| spellingShingle | Kaiqiang Wang Li Song Chutian Wang Zhenbo Ren Guangyuan Zhao Jiazhen Dou Jianglei Di George Barbastathis Renjie Zhou Jianlin Zhao Edmund Y. Lam On the use of deep learning for phase recovery Light: Science & Applications |
| title | On the use of deep learning for phase recovery |
| title_full | On the use of deep learning for phase recovery |
| title_fullStr | On the use of deep learning for phase recovery |
| title_full_unstemmed | On the use of deep learning for phase recovery |
| title_short | On the use of deep learning for phase recovery |
| title_sort | on the use of deep learning for phase recovery |
| url | https://doi.org/10.1038/s41377-023-01340-x |
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