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Artificial Intelligence Enabled Information Inpainting and Artifact Removal for Electron Tomography

Published online by Cambridge University Press:  30 July 2020

Huolin Xin ,
He Wei ,
Guanglei Ding ,
Chunyang Wang ,
Yitong Liu  and
Rui Zhang
Huolin Xin
Affiliation:
University of California - Irvine, Irvine, California, United States
He Wei
Affiliation:
University of California - Irvine, Irvine, California, United States
Guanglei Ding
Affiliation:
University of California - Irvine, Irvine, California, United States
Chunyang Wang
Affiliation:
University of California - Irvine, Irvine, California, United States
Yitong Liu
Affiliation:
Beijing University of Posts and Teleconmmunications, Beijing, California, United States
Rui Zhang
Affiliation:
University of California - Irvine, Irvine, California, United States

Abstract

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Type
FIB-SEM Technology and Electron Tomography for Materials Science and Engineering
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 664 - 665
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Copyright
Copyright © Microscopy Society of America 2020

References

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Goris, B., Van den Broek, W., Batenburg, K. J., Mezerji, H. H. & Bals, S. Electron tomography based on a total variation minimization reconstruction technique. Ultramicroscopy 113, 120-130 (2012).10.1016/j.ultramic.2011.11.004CrossRefGoogle Scholar
Ding, G., Liu, Y., Zhang, R. & Xin, H. L. A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond. Scientific reports 9, 12803, doi:10.1038/s41598-019-49267-x (2019).CrossRefGoogle ScholarPubMed