Skip to main content Accessibility help
×
Home
Hostname: page-component-66d7dfc8f5-tqznl Total loading time: 0.907 Render date: 2023-02-09T00:02:27.720Z Has data issue: true Feature Flags: { "useRatesEcommerce": false } hasContentIssue true

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
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

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
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
You have Access

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

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

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

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

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *