计算机科学
多模光纤
卷积神经网络
人工智能
忠诚
人工神经网络
模式识别(心理学)
网络体系结构
迭代重建
图像(数学)
集合(抽象数据类型)
高保真
斑点图案
联营
计算机视觉
光纤
电信
计算机网络
工程类
电气工程
程序设计语言
作者
Changyan Zhu,Eng Aik Chan,You Wang,Weina Peng,Rui Guo,Baile Zhang,Cesare Soci,Yidong Chong
标识
DOI:10.1038/s41598-020-79646-8
摘要
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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