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Deep-Learning-Based Imaging through Glass-Air Disordered Fiber with Transverse Anderson Localization

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Abstract

We demonstrate for the first time that deep neural networks (DNNs) can be trained to recover images transported through a 90 cm-long silica-air disordered optical fiber at variable working distances without any distal optics.

© 2018 The Author(s)

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