Deep learning assisted image transmission in multimode fibers

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.


Published in:
Adaptive Optics And Wavefront Control For Biological Systems V, 10886, 108860N
Presented at:
Conference on Adaptive Optics and Wavefront Control for Biological Systems V, held at SPIE BiOS, San Francisco, CA, Feb 03-04, 2019
Year:
Jan 01 2019
Publisher:
Bellingham, SPIE-INT SOC OPTICAL ENGINEERING
ISSN:
0277-786X
1996-756X
ISBN:
978-1-5106-2414-6
978-1-5106-2415-3
Keywords:
Laboratories:




 Record created 2019-09-26, last modified 2019-09-30


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