open access

https://hdl.handle.net/2115/85768

Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning

英語

transmission electron microscopy

2021-12-10

Cambridge University Press

Microscopy and microanalysis

28(1)

138-144

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 1,000 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.

学術雑誌論文

Accepted Manuscript

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

This article has been published in a revised form in Microscopy and microanalysis https://doi.org/10.1017/S1431927621013799. This version is published under a Creative Commons CC-BY-NC-ND. No commercial re-distribution or re-use allowed. Derivative works cannot be distributed. ©The Author(s), 2021.

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