Deep convolutional neural network image processing method providing improved signal-to-noise ratios in electron holography
Deep convolutional neural network image processing method providing improved signal-to-noise ratios in electron holography
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DOI：
10.1093/jmicro/dfab012
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发表时间：
2021-03-17
期刊：
影响因子：
1.8
通讯作者：
Murakami, Yasukazu
中科院分区：
文献类型：
--
作者：
Asari, Yusuke;Terada, Shohei;Murakami, Yasukazu
利用深度卷积神经网络（CNN）开发了一种图像识别方法，并将其应用于使用电子全息术的无机颗粒分析。尽管通过透射电子显微镜观察到的α-Fe 2 O3颗粒的形状存在显着变化，但这种基于CNN的方法可用于识别孤立的纺锤形颗粒，这些颗粒与其他经历配对和/或团聚的颗粒不同。这些孤立的粒子的图像的平均提供了一个显着的改善相位分析精度的电子全息观察。该方法有望在分析由仅显示小相移的纳米颗粒产生的弱电磁场方面有所帮助。
An image identification method was developed with the aid of a deep convolutional neural network (CNN) and applied to the analysis of inorganic particles using electron holography. Despite significant variation in the shapes of alpha-Fe2O3 particles that were observed by transmission electron microscopy, this CNN-based method could be used to identify isolated, spindle-shaped particles that were distinct from other particles that had undergone pairing and/or agglomeration. The averaging of images of these isolated particles provided a significant improvement in the phase analysis precision of the electron holography observations. This method is expected to be helpful in the analysis of weak electromagnetic fields generated by nanoparticles showing only small phase shifts.