Deep neural networks for seeing through multimode fibers

Eirini Kakkava; Navid Borhani; Christophe Moser; Demetri Psaltis

doi:10.1117/12.2509934

4 March 2019 Deep neural networks for seeing through multimode fibers

Eirini Kakkava, Navid Borhani, Christophe Moser, Demetri Psaltis

Proceedings Volume 10889, High-Speed Biomedical Imaging and Spectroscopy IV; 108891A (2019) https://doi.org/10.1117/12.2509934
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Image delivery through multimode fibers (MMFs) suffers from modal scrambling which results in a speckle pattern at the fiber output. In this work, we use Deep Neural Networks (DNNs) for recovery and/or classification of the input image from the intensity-only images of the speckle patterns at the distal end of the fiber. We train the DNNs using 16,000 images of handwritten digits of the MNIST database and we test the accuracy of classification and reconstruction on another 2,000 new digits. Very positive results and robustness were observed for up to 1 km long MMF showing 90% reconstruction fidelity. The classification accuracy of the system for different inputs (phase-only, amplitude-only, hologram intensity etc.) to the DNN classifier was also tested.

Citation Download Citation

Eirini Kakkava, Navid Borhani, Christophe Moser, and Demetri Psaltis "Deep neural networks for seeing through multimode fibers", Proc. SPIE 10889, High-Speed Biomedical Imaging and Spectroscopy IV, 108891A (4 March 2019); https://doi.org/10.1117/12.2509934

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