Advanced Photonics, Volume. 2, Issue 2, 020501(2020)

Solving the missing cone problem by deep learning

Dashan Dong1,2 and Kebin Shi1,2,3、*
Author Affiliations
  • 1Peking University, State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-Optoelectronics, School of Physics, Beijing, China
  • 2Shanxi University, Collaborative Innovation Center of Extreme Optics, Taiyuan, Shanxi, China
  • 3Peking University, Collaborative Innovation Center of Quantum Matter, Beijing, China
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    Figures & Tables(1)
    Schematic diagram of “missing cone” in optical diffractive tomography (ODT). (a), (b) Schematic diagram of ODT with rotating illumination (a) and rotating sample (b). (c), (d) The spatial scattering spectrum captured by the objective: red arrows indicate the transmitted and scattered signal projected on a spherical cap; the numerical aperture of the objective limits the angular bandwidth of the scattered signal. (e), (f) ODT casts spatial spectrum caps from different rotation directions together in order to reconstruct the 3-D spatial spectrum of the sample. The missing spectrum along the rotation axis is observed with both illumination rotation (e) and sample rotation (f).
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    Dashan Dong, Kebin Shi, "Solving the missing cone problem by deep learning," Adv. Photon. 2, 020501 (2020)

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    Paper Information

    Category: News and Commentaries

    Received: --

    Accepted: --

    Published Online: Apr. 30, 2020

    The Author Email: Shi Kebin (kebinshi@pku.edu.cn)

    DOI:10.1117/1.AP.2.2.020501

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