HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Institute of Low Temperature Science >
Peer-reviewed Journal Articles, etc >

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

This item is licensed under:Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Files in This Item:
Microscopy and microanalysis_28(1)_138-144.pdf1.09 MBPDFView/Open
Please use this identifier to cite or link to this item:

Title: Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning
Authors: Katsuno, Hiroyasu Browse this author →KAKEN DB
Kimura, Yuki Browse this author →KAKEN DB
Yamazaki, Tomoya Browse this author
Takigawa, Ichigaku Browse this author →KAKEN DB
Keywords: deep learning
fast improvement
transmission electron microscopy
Issue Date: 10-Dec-2021
Publisher: Cambridge University Press
Journal Title: Microscopy and microanalysis
Volume: 28
Issue: 1
Start Page: 138
End Page: 144
Publisher DOI: 10.1017/S1431927621013799
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.
Rights: This article has been published in a revised form in Microscopy and microanalysis 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.
Type: article (author version)
Appears in Collections:低温科学研究所 (Institute of Low Temperature Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 勝野 弘康

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 - Hokkaido University