Laser & Optoelectronics Progress, Volume. 58, Issue 18, 1811020(2021)

Fourier Ptychography Based on Deep Learning

hao Sha1, Yangzhe Liu2, and Yongbing Zhang1、*
Author Affiliations
  • 1School of Computer of Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
  • 2Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China
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    Figures & Tables(9)
    Model of the FP forward imaging
    Reconstruction process of the FP image
    FP neural network based on physical model
    Simulation results of different algorithms. (a) INNM; (b) ePIE; (c) original high-resolution image; (d) sDR; (e) real image
    Simulation results of different aperture overlap ratios. (a) Real image; (b) 25%; (c) 50%; (d) 70%; (e) 85%
    Reconstruction results of ablation experiments. (a) Zernike polynomial and TV term are not introduced; (b) Zernike polynomial is introduced separately; (c) Zernike polynomial and TV term are introduced at the same time; (d) original high-resolution image
    Optical path of the macro FP
    Experimental setup of the macro FP
    Reconstruction result of USAF resolution target. (a) Low-resolution image; (b) enlarged detail 1 of the USAF; (c) ePIE; (d) INNM; (e) enlarged detail 2 of the USAF; (f) reference image
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    hao Sha, Yangzhe Liu, Yongbing Zhang. Fourier Ptychography Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2021, 58(18): 1811020

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    Paper Information

    Category: Imaging Systems

    Received: Jun. 2, 2021

    Accepted: Jul. 20, 2021

    Published Online: Aug. 28, 2021

    The Author Email: Zhang Yongbing (ybzhang08@hit.edu.cn)

    DOI:10.3788/LOP202158.1811020

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