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Title: Image reconstruction through a multimode fiber with a simple neural network architecture
Authors: Zhu, Changyan
Chan, Eng Aik
Wang, You
Peng, Weina
Guo, Ruixiang
Zhang, Baile
Soci, Cesare
Chong, Yidong
Keywords: Science::Physics
Issue Date: 2021
Source: Zhu, C., Chan, E. A., Wang, Y., Peng, W., Guo, R., Zhang, B., . . . Chong, Y. (2021). Image reconstruction through a multimode fiber with a simple neural network architecture. Scientific Reports, 11(1), 896-. doi:10.1038/s41598-020-79646-8
Project: MOE2016‐T3‐1‐006 
Journal: Scientific Reports 
Abstract: Multimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.
ISSN: 2045-2322
DOI: 10.1038/s41598-020-79646-8
Schools: School of Physical and Mathematical Sciences 
Research Centres: Centre for Disruptive Photonic Technologies (CDPT) 
Rights: © 2021 The Author(s). 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit
Fulltext Permission: open
Fulltext Availability: With Fulltext
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