论文标题
通过简单神经网络体系结构的多模纤维重建图像重建
Image reconstruction through a multimode fiber with a simple neural network architecture
论文作者
论文摘要
多模纤维（MMFS）具有用于内窥镜检查和相关应用的复杂图像的潜力，但是解码MMFS中模式混合和模态分散产生的复杂斑点模式是一个严重的挑战。几个小组最近表明，卷积神经网络（CNN）可以训练以执行高保真性MMF图像重建。我们发现，一个更简单的神经网络体系结构，即单个隐藏层密集的神经网络，至少在图像重建保真度方面和以前使用的CNN都表现出色，并且在训练时间和计算资源方面表现出色。训练组停止后一周内收集的MMF图像可以准确地重建MMF图像，并且密集网络的性能以及整个时期的CNN。
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.
