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. 2018 Oct 3;7:69. doi: 10.1038/s41377-018-0074-1

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Performance of the network in reconstructing the input phases from the output amplitude speckle patterns when the CNN is trained with the handwritten Latin alphabet. The speckle pattern for each letter image is obtained using the transmission matrix of the system. Calculated (a) MSE and (b) 2D-correlation for the train/validation data sets. c Examples of the output amplitude speckle patterns and the reconstructed fiber input phase patterns produced via the CNN. The fidelity number for each reconstructed image with respect to its corresponding grayscale label is shown