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Deep learning assisted image transmission in multimode fibers
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Paper Abstract

We propose a data-driven approach for light transmission control inside multimode fibers (MMFs). Specifically, we show that a convolutional neural network is able to reconstruct amplitude/phase modulated images from scrambled amplitude-only images obtained at the output of a 0.75m long MMF with a fidelity (correlation) as high as ~98%. We show that the trained network shows good generalization as well. In particular, it is shown that the network is able to reconstruct images that do not belong to train/test datasets.

Paper Details

Date Published: 20 February 2019
PDF: 7 pages
Proc. SPIE 10886, Adaptive Optics and Wavefront Control for Biological Systems V, 108860N (20 February 2019); doi: 10.1117/12.2508383
Show Author Affiliations
Babak Rahmani, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Damien Loterie, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Georgia Konstantinou, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Demetri Psaltis, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Christophe Moser, Ecole Polytechnique Fédérale de Lausanne (Switzerland)


Published in SPIE Proceedings Vol. 10886:
Adaptive Optics and Wavefront Control for Biological Systems V
Thomas G. Bifano; Sylvain Gigan; Na Ji, Editor(s)

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