利用神经网络和菲涅耳法全息重建光弹性色散系数

Netcom Pub Date : 2023-07-30 DOI:10.1109/OMN/SBFotonIOPC58971.2023.10230956

F. Prado, Pedro Henrique Miho De Souza, S. L. Da Silva, N. Wetter

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引用次数: 0

摘要

在这里，我们报告了使用两种不同的重建方法的数字全息光弹性色散系数的表征:一种基于菲涅耳方法，另一种利用卷积神经网络(CNN)。CNN使用菲涅耳方法进行重建训练，能够提供平均均方误差为0.006的重建。

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Photoelastic Dispersion Coefficient by Holographic Reconstruction with Neural Networks and the Fresnel Method

Here we report the characterization of the photoelastic dispersion coefficient using digital holography with two distinct reconstruction methods: one based on the Fresnel method and the other utilizing convolutional neural networks (CNN). The CNN was trained with reconstruction from the Fresnel method and was able to provide reconstructions with an average Mean Squared Error of 0.006.

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Netcom

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0.00%

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审稿时长

18 weeks