Deep learning assisted image transmission in multimode fibers

Babak Rahmani; Damien Loterie; Georgia Konstantinou; Demetri Psaltis; Christophe Moser

doi:10.1117/12.2508383

20 February 2019 Deep learning assisted image transmission in multimode fibers

Babak Rahmani, Damien Loterie, Georgia Konstantinou, Demetri Psaltis, Christophe Moser

Proceedings Volume 10886, Adaptive Optics and Wavefront Control for Biological Systems V; 108860N (2019) https://doi.org/10.1117/12.2508383
Event: SPIE BiOS, 2019, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Babak Rahmani, Damien Loterie, Georgia Konstantinou, Demetri Psaltis, and Christophe Moser "Deep learning assisted image transmission in multimode fibers", Proc. SPIE 10886, Adaptive Optics and Wavefront Control for Biological Systems V, 108860N (20 February 2019); https://doi.org/10.1117/12.2508383

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