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arXiv · 2006.05708

Image reconstruction through a multimode fiber with a simple neural network architecture

Changyan Zhu·Eng Aik Chan·You Wang·Weina Peng·Ruixiang Guo·Baile Zhang·Cesare Soci·Yidong Chong
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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.

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Changyan Zhu, Eng Aik Chan, You Wang, Weina Peng, Ruixiang Guo, Baile Zhang, Cesare Soci, Yidong Chong. 2020-06-10. Image reconstruction through a multimode fiber with a simple neural network architecture. https://arxiv.org/abs/2006.05708

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