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

Published online by Cambridge University Press:  10 December 2021

Hiroyasu Katsuno Open the ORCID record for Hiroyasu Katsuno [Opens in a new window] ,
Yuki Kimura Open the ORCID record for Yuki Kimura [Opens in a new window] ,
Tomoya Yamazaki Open the ORCID record for Tomoya Yamazaki [Opens in a new window]  and
Ichigaku Takigawa
Hiroyasu Katsuno*
Affiliation:
Institute of Low Temperature Science, Hokkaido University, Kita-19, Nishi-8, Kita-ku, Sapporo, Hokkaido 060-0819, Japan
Yuki Kimura
Affiliation:
Institute of Low Temperature Science, Hokkaido University, Kita-19, Nishi-8, Kita-ku, Sapporo, Hokkaido 060-0819, Japan
Tomoya Yamazaki
Affiliation:
Institute of Low Temperature Science, Hokkaido University, Kita-19, Nishi-8, Kita-ku, Sapporo, Hokkaido 060-0819, Japan
Ichigaku Takigawa
Affiliation:
RIKEN, Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-Ku, Tokyo 103-0027, Japan Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, N21 W10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
*
*Corresponding author: Hiroyasu Katsuno, E-mail: [email protected]
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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 $1{,}000$ $e^{-}$ 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.

Keywords

deep learningfast improvementnanoparticlestransmission electron microscopy
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 28 , Issue 1 , February 2022 , pp. 138 - 144
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

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