Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-23T10:44:19.705Z Has data issue: false hasContentIssue false
  • English
  • Français

Developing Deep Neural Network-based Denoising Techniques for Time-Resolved In Situ TEM of Catalyst Nanoparticles

Published online by Cambridge University Press:  30 July 2021

Joshua Vincent ,
Sreyas Mohan ,
Ramon Manzorro ,
Binh Tang ,
Dev Sheth ,
Mitesh Khapra ,
David Matteson ,
Eero Simoncelli ,
Carlos Fernandez-Granda  and
Peter Crozier
Joshua Vincent
Affiliation:
Arizona State University, United States
Sreyas Mohan
Affiliation:
New York University, United States
Ramon Manzorro
Affiliation:
Arizona State University, United States
Binh Tang
Affiliation:
Cornell University, United States
Dev Sheth
Affiliation:
IIT Madras, United States
Mitesh Khapra
Affiliation:
IIT Madras, United States
David Matteson
Affiliation:
Cornell University, United States
Eero Simoncelli
Affiliation:
New York University, United States
Carlos Fernandez-Granda
Affiliation:
New York University, United States
Peter Crozier
Affiliation:
Arizona State University, Tempe, Arizona, United States

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 262 - 264
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Faruqi, A. and McMullan, G., Nucl. Instrum. Methods Phys. Res, 878 (2018), p. 180-190.CrossRefGoogle Scholar
Lawrence, E. L., et al. , Microscopy and Microanalysis, 26 (2020), p. 86-94.CrossRefGoogle Scholar
Liu, B. and Liu, J. Journal of Physics: Conference Series, 1176:022010 (2019).Google Scholar
Tian, C., et al. , arXiv:1912.13171v4, (2019).Google Scholar
Ede, J., Machine Learning: Science and Technology, (2020), in press.Google Scholar
Vincent, J. L., et al. , in preparation, pre-print at: arXiv :2101.07770, (2021).Google Scholar
Mohan, S., et al. , in preparation, pre-print at: arXiv:2010.12970, (2020).Google Scholar
Sheth, Dev Y., et al. , submitted to CVPR, pre-print at: arXiv:2011.15045, (2020).Google Scholar
We gratefully acknowledge support of NSF grants CBET-1604971, NRT-1922658, CCF-1934985, OAC-1940097, OAC-1940124, and OAC-1940263, and the facilities at ASU's John M. Cowley Center for High Resolution Electron Microscopy.Google Scholar