首页|On the use of deep learning for phase recovery

On the use of deep learning for phase recovery

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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.

Kaiqiang Wang、Li Song、Chutian Wang、Zhenbo Ren、Guangyuan Zhao、Jiazhen Dou、Jianglei Di、George Barbastathis、Renjie Zhou、Jianlin Zhao、Edmund Y.Lam

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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

School of Information Engineering,Guangdong University of Technology,Guangzhou,China

Department of Mechanical Engineering,Massachusetts Institute of Technology,Cambridge,MA,USA

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National Natural Science Foundation of ChinaResearch Grants Council of Hong KongResearch Grants Council of Hong KongResearch Grants Council of Hong KongHong Kong Innovation and Technology Fund

61927810GRF 17201620GRF 17200321RIF R7003-21ITS/148/20

2024

10.1038/s41377-023-01340-x

光:科学与应用(英文版)
中国科学院长春光学精密机械与物理研究所

光:科学与应用(英文版)

CSTPCD
ISSN：2095-5545
年,卷(期)：2024.13(2)
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