Multimode fiber projection with machine learning

Babak Rahmani; Damien Loterie; Eirini Kakkava; Navid Borhani; Ugur Tegin; Ugur Tegin; Demetri Psaltis; Christophe Moser

doi:10.1364/DH.2020.HTu5B.3

Imaging and Applied Optics Congress
OSA Technical Digest (Optica Publishing Group, 2020),
paper HTu5B.3
•https://doi.org/10.1364/DH.2020.HTu5B.3

Multimode fiber projection with machine learning

Babak Rahmani, Damien Loterie, Eirini Kakkava, Navid Borhani, Ugur Tegin, Demetri Psaltis, and Christophe Moser

Digital Holography and Three-Dimensional Imaging 2020

Washington, DC United States
22–26 June 2020
ISBN: 978-1-943580-77-4

From the session
Learning-based Approaches in Digital Holography II (HTu5B)

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B. Rahmani, D. Loterie, E. Kakkava, N. Borhani, U. Tegin, D. Psaltis, and C. Moser, "Multimode fiber projection with machine learning," in Imaging and Applied Optics Congress, OSA Technical Digest (Optica Publishing Group, 2020), paper HTu5B.3.

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Abstract

We propose an imaging method for controlling the output of scattering media such as multimode fibers using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement methods.

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