Solving the missing cone problem by deep learning

Dashan Dong; Kebin Shi

doi:10.1117/1.AP.2.2.020501

Journals >Advanced Photonics >Volume 2 >Issue 2 >Page 020501 > Article

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

Solving the missing cone problem by deep learning

Dashan Dong^1、2 and Kebin Shi^{1、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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DOI: 10.1117/1.AP.2.2.020501 Cite this Article Set citation alerts

Dashan Dong, Kebin Shi. Solving the missing cone problem by deep learning[J]. Advanced Photonics, 2020, 2(2): 020501 Copy Citation Text

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References

[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).

Dashan Dong, Kebin Shi. Solving the missing cone problem by deep learning[J]. Advanced Photonics, 2020, 2(2): 020501

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