Randomized Probe Imaging through Deep K-Learning

Zhen Guo; Abraham Levitan; George Barbastathis; Riccardo Comin

doi:10.1364/COSI.2021.CTh7A.6

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper CTh7A.6
•https://doi.org/10.1364/COSI.2021.CTh7A.6

Randomized Probe Imaging through Deep K-Learning

Zhen Guo, Abraham Levitan, George Barbastathis, and Riccardo Comin

Computational Optical Sensing and Imaging 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Lensless Imaging (CTh7A)

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Z. Guo, A. Levitan, G. Barbastathis, and R. Comin, "Randomized Probe Imaging through Deep K-Learning," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper CTh7A.6.

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Abstract

Iterative phase retrieval algorithms are time-consuming. To accelerate reconstructions for Randomized Probe Imaging (RPI), we propose deep k-learning, a neural network with attention to frequency. The associated computational speedup may enable fast dynamical imaging.

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