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Application of Deep Unsupervised Convolutional Neural Networks to Denoise Large Temporally Resolved In Situ TEM Datasets

Published online by Cambridge University Press:  22 July 2022

Adrià Marcos Morales
Affiliation:
New York University, New York, United States
Advait Gilankar*
Affiliation:
Arizona State University, Tempe, AZ, United States
Ramon Manzorro
Affiliation:
Arizona State University, Tempe, AZ, United States
Piyush Haluai
Affiliation:
Arizona State University, Tempe, AZ, United States
Mai Tan
Affiliation:
Arizona State University, Tempe, AZ, United States
Joshua L. Vincent
Affiliation:
Arizona State University, Tempe, AZ, United States
Carlos Fernandez-Granda
Affiliation:
New York University, New York, United States
Peter A. Crozier
Affiliation:
Arizona State University, Tempe, AZ, United States
*
*Corresponding author: agilanka@asu.edu

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Copyright
Copyright © Microscopy Society of America 2022

References

Vincent, JL et al. , Nature Communications 12(5789) (2021).Google Scholar
Alexandrova, AN et al. , ACS Catalysis, (7)3 (2017), p. 1905.Google Scholar
Vincent, JL et al. , Microscopy and Microanalysis, (27)6, p. 1431.10.1017/S1431927621012678CrossRefGoogle Scholar
Sheth, DY et al. , arXiv:2011.15045v3 [eess.IV]Google Scholar
We gratefully acknowledge the support of the following NSF grants (OAC 1940263, OAC 1940097, CBET 1604971 and DMR 184084). We also acknowledge the support from DOE grant BES DE-SC0004954. The authors acknowledge HPC resources available through ASU, and NYU as well as the John M. Cowley Center for High Resolution Electron Microscopy at Arizona State.Google Scholar