Multimode optical fiber transmission with a deep learning network

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

doi:10.1038/s41377-018-0074-1

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Multimode optical fiber transmission with a deep learning network

Original Articles

- Multimode optical fiber transmission with a deep learning network
- Multimode optical fiber transmission with a deep learning network
- 光：科学与应用 2018年7卷第6期页码：738-748
- Affiliations：
  
  1.Ecole Polytechnique Fédérale de Lausanne, Laboratory of Applied Photonics Devices, CH-1015, Lausanne, Switzerland
  2.Ecole Polytechnique Fédérale de Lausanne, Laboratory of Optics, CH-1015, Lausanne, Switzerland
- Author bio：
  
  Babak Rahmani (babak.rahmani@epfl.ch)
- Funds：
- DOI：10.1038/s41377-018-0074-1
  中图分类号：
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Babak Rahmani, Damien Loterie, Georgia Konstantinou, 等. Multimode optical fiber transmission with a deep learning network[J]. 光：科学与应用, 2018,7(6):738-748.

Rahmani, B. et al. Multimode optical fiber transmission with a deep learning network. Light: Science & Applications, 6, 738-748 (2018).
Babak Rahmani, Damien Loterie, Georgia Konstantinou, 等. Multimode optical fiber transmission with a deep learning network[J]. 光：科学与应用, 2018,7(6):738-748. DOI： 10.1038/s41377-018-0074-1.

Rahmani, B. et al. Multimode optical fiber transmission with a deep learning network. Light: Science & Applications, 6, 738-748 (2018). DOI： 10.1038/s41377-018-0074-1.

摘要

Abstract

Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent light propagating within them to produce seemingly random patterns. Thus, for applications such as imaging and image projection through an MMF, careful measurements of the relationship between the inputs and outputs of the fiber are required. We show, as a proof of concept, that a deep neural network can learn the input-output relationship in a 0.75 m long MMF. Specifically, we demonstrate that a deep convolutional neural network (CNN) can learn the nonlinear relationships between the amplitude of the speckle pattern (phase information lost) obtained at the output of the fiber and the phase or the amplitude at the input of the fiber. Effectively, the network performs a nonlinear inversion task. We obtained image fidelities (correlations) as high as ~98% for reconstruction and ~94% for image projection in the MMF compared with the image recovered using the full knowledge of the system transmission characterized with the complex measured matrix. We further show that the network can be trained for transfer learning, i.e., it can transmit images through the MMF, which belongs to another class not used for training/testing.

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references

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