Solving the missing cone problem by deep learning

Dashan Dong; Kebin Shi

doi:10.1117/1.AP.2.2.020501

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

Solving the missing cone problem by deep learning

Dashan Dong^1,2 and Kebin Shi^1,2,3、*

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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References(5)

[1] J. Lim et al. Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography. Opt. Express, 23, 16933-16948(2015).

[2] Y. Sung, R. R. Dasari. Deterministic regularization of three-dimensional optical diffraction tomography. J. Opt. Soc. Am. A, 28, 1554-1561(2011).

[3] J. Lim, A. Ayoub, D. Psaltis. Three-dimensional tomography of red blood cells using deep learning. Adv. Photonics, 2, 026001(2020).

[4] F. Merola et al. Tomographic flow cytometry by digital holography. Light Sci. Appl., 6, e16241(2017).

[5] D. Dong et al. Super-resolution fluorescence-assisted diffraction computational tomography reveals the three-dimensional landscape of the cellular organelle interactome. Light Sci. Appl., 9, 11(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

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology