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
- Light: Science & Applications Vol. 7, Issue 6, Pages: 738-748(2018)
- 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
  CLC：
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Rahmani, B. et al. Multimode optical fiber transmission with a deep learning network. Light: Science & Applications, 6, 738-748 (2018).
DOI：

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

Spitz, E. & Werts, A. Transmission des images à travers une fibre optique. Comptes Rendus Hebd. Des. Seances De. L Acad. Des. Sci. Ser. B 264, 1015 (1967).

Yariv, A. On transmission and recovery of three-dimensional image information in optical waveguides. J. Opt. Soc. Am. 66, 301–306 (1976).

Gover, A., Lee, C. P. & Yariv, A. Direct transmission of pictorial information in multimode optical fibers. J. Opt. Soc. Am. 66, 306–311 (1976).

Dunning, G. J. & Lind, R. C. Demonstration of image transmission through fibers by optical phase conjugation. Opt. Lett. 7, 558–560 (1982).

Friesem, A. A., Levy, U. & Silberberg, Y. Parallel transmission of images through single optical fibers. Proc. IEEE 71, 208–221 (1983).

Di Leonardo, R. & Bianchi, S. Hologram transmission through multi-mode optical fibers. Opt. Express 19, 247–254 (2011).

Čižmár, T. & Dholakia, K. Shaping the light transmission through a multimode optical fibre: Complex transformation analysis and applications in biophotonics. Opt. Express 19, 18871–18884 (2011).

Čižmár, T. & Dholakia, K. Exploiting multimode waveguides for pure fibre-based imaging. Nat. Commun. 3, 1027 (2012).

Bianchi, S. & Di Leonardo, R. A multi-mode fiber probe for holographic micromanipulation and microscopy. Lab. Chip. 12, 635–639 (2012).

Andresen, E. R., Bouwmans, G., Monneret, S. & Rigneault, H. Toward endoscopes with no distal optics: Video-rate scanning microscopy through a fiber bundle. Opt. Lett. 38, 609–611 (2013).

Papadopoulos, I. N., Farahi, S., Moser, C. & Psaltis, D. Focusing and scanning light through a multimode optical fiber using digital phase conjugation. Opt. Express 20, 10583–10590 (2012).

Papadopoulos, I. N., Farahi, S., Moser, C. & Psaltis, D. High-resolution, lensless endoscope based on digital scanning through a multimode optical fiber. Biomed. Opt. Express 4, 260–270 (2013).

Choi, Y. et al. Scanner-free and wide-field endoscopic imaging by using a single multimode optical fiber.Phys. Rev. Lett. 109, 203901 (2012).

Caravaca-Aguirre, A. M., Niv, E., Conkey, D. B. & Piestun, R. Real-time resilient focusing through a bending multimode fiber. Opt. Express 21, 12881–12887 (2013).

Gu, R. Y., Mahalati, R. N. & Kahn, J. M. Design of flexible multi-mode fiber endoscope. Opt. Express 23, 26905–26918 (2015).

Loterie, D. et al. Digital confocal microscopy through a multimode fiber. Opt. Express 23, 23845–23858 (2015).

Popoff, S., Lerosey, G., Fink, M., Boccara, A. C. & Gigan, S. Image transmission through an opaque material. Nat. Commun. 1, 81 (2010).

N'gom, M. et al. Controlling light transmission through highly scattering media using semi-definite programming as a phase retrieval computation method. Sci. Rep. 7, 2518 (2017).

N'Gom, M., Norris, T. B., Michielssen, E. & Nadakuditi, R. R. Mode control in a multimode fiber through acquiring its transmission matrix from a reference-less optical system. Opt. Lett. 43, 419–422 (2018).

Aisawa, S., Noguchi, K. & Matsumoto, T. Remote image classification through multimode optical fiber using a neural network. Opt. Lett. 16, 645–647 (1991).

Matsumoto, T., Koga, M., Noguchi, K., Aizawa, S. Proposal for neural-network applications to fiber-optic transmission. In Proc. of 1990 IJCNN International Joint Conference on Neural Networks; (IEEE, San Diego, CA, USA, 1990).

Marusarz, R. K. & Sayeh, M. R. Neural network-based multimode fiber-optic information transmission. Appl. Opt. 40, 219–227 (2001).

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

McCann, M. T., Jin, K. H. & Unser, M. Convolutional neural networks for inverse problems in imaging: A review. IEEE Signal Process Mag. 34, 85–95 (2017).

Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437–1443 (2017).

Sinha, A., Lee, J., Li, S. & Barbastathis, G. Lensless computational imaging through deep learning. Optica 4, 1117–1125 (2017).

Rivenson, Y., Zhang, Y. B., Günaydın, H., Teng, D. & Ozcan, A. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light.: Sci. Appl. 7, 17141 (2018).

Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv Prepr. arXiv 1409, 1556 (2014).

He, K. M., Zhang, X. Y., Ren, S. Q., Sun, J. Deep residual learning for image recognition. In Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition; 770–778 (IEEE: Las Vegas, NV, USA, 2016).

Xie, S. N., Girshick, R., Dollár, P., Tu, Z. W., He, K. M. Aggregated residual transformations for deep neural networks. InProceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 5987–5995 (IEEE: Honolulu, Hawaii, USA, 2017).

He, K., Zhang, X., Ren, S., Sun, J. Identity mappings in deep residual networks. In European Conference on Computer Vision (ed. Leibe, B., Matas, J., Sebe N., Welling, M.) 630–645 (Springer, Cham, 2016).

Cohen, G., Afshar, S., Tapson, J., van Schaik, A. EMNIST: An extension of MNIST to handwritten letters. Preprint at https://arxiv.org/abs/1702.05373https://arxiv.org/abs/1702.05373 (2017).

Saleh, B. E. A. & Teich, M. C. Fundamentals of Photonics.. (Wiley, New York, 1991).

Plöschner, M., Tyc, T. & Čižmár, T. Seeing through chaos in multimode fibres. Nat. Photonics 9, 529–535 (2015).

Krizhevsky, A., Sutskever, I., Hinton, G. E. ImageNet classification with deep convolutional neural networks. inProceedings of the 25th International Conference on Neural Information Processing Systems; 3–6 December 2012 1097–1105 (Curran Associates Inc.: Lake Tahoe, Nevada, 2012).

Graham, B. Fractional max-pooling. Preprint at https://arxiv.org/abs/1412.6071https://arxiv.org/abs/1412.6071 (2014).

Kingma, D. P., Ba, J. Adam: A method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980https://arxiv.org/abs/1412.6980 2014.

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