Color image identification and reconstruction using artificial neural networks on multimode fiber images: Towards an all-optical design

Nadav Shabairou, Eyal Cohen, Omer Wagner, Dror Malka, Zeev Zalevsky

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

The rapid growth of applications that rely on artificial neural network (ANN) concepts gives rise to a staggering increase in the demand for hardware implementations of neural networks. New types of hardware that can support the requirements of high-speed associative computing while maintaining low power consumption are sought, and optical artificial neural networks fit the task well. Inherently, optical artificial neural networks can be faster, support larger bandwidth, and produce less heat than their electronic counterparts. Here we propose the design of an optical ANN-based imaging system that has the ability to self-study image signals from an incoherent light source in different colors. Our design consists of a combination of a multimode fiber and a multi-core optical fiber realizing a neural network. We show that the signals, transmitted through the multimode fiber, can be used for image identification purposes and can also be reconstructed using ANNs with a low number of nodes. An all-optical solution can then be achieved by realizing these networks with the multi-core optical neural network fiber.

Original languageEnglish
Pages (from-to)5603-5606
Number of pages4
JournalOptics Letters
Volume43
Issue number22
DOIs
StatePublished - 15 Nov 2018

Bibliographical note

Publisher Copyright:
© 2018 Optical Society of America

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