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arXiv · 2106.01718

Fast improvement of TEM image with low-dose electrons by deep learning

Hiroyasu Katsuno·Yuki Kimura·Tomoya Yamazaki·Ichigaku Takigawa
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Abstract

Low-electron-dose observation is indispensable for observing various samples using a transmission electron microscope; consequently, image processing has been used to improve transmission electron microscopy (TEM) images. To apply such image processing to in situ observations, we here apply a convolutional neural network to TEM imaging. Using a dataset that includes short-exposure images and long-exposure images, we develop a pipeline for processed short-exposure images, based on end-to-end training. The quality of images acquired with a total dose of approximately 5 e- per pixel becomes comparable to that of images acquired with a total dose of approximately 1000 e- per pixel. Because the conversion time is approximately 8 ms, in situ observation at 125 fps is possible. This imaging technique enables in situ observation of electron-beam-sensitive specimens.

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Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa. 2021-06-03. Fast improvement of TEM image with low-dose electrons by deep learning. https://doi.org/10.1017/s1431927621013799

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