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Deep Learning as a Tool for Image Denoising and Drift Correction

Published online by Cambridge University Press:  05 August 2019

Rama K. Vasudevan*
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
Stephen Jesse
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
*
*Corresponding author: [email protected]

Abstract

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Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Jones, L and Nellist, PD, Microsc. Microanal. 19 (2013), p. 1050.Google Scholar
[2]Sang, X and LeBeau, JM, Ultramicroscopy 138 (2014), p. 28.Google Scholar
[3]Dyck, O et al. , Appl. Phys. Lett. 111 (2017), p. 113104.Google Scholar
[4]Ziatdinov, M et al. , ACS Nano 11 (2017), p. 12742.Google Scholar
[5]Ziatdinov, M et al. , npj Comp. Mater. 5 (2019), p. 12.Google Scholar
[6]LeCun, Y, Bengio, Y and Hinton, G, Nature 521 (2015), p. 436.Google Scholar
[7]This work was conducted at and supported by the Center for Nanophase Materials Sciences, which is a US DOE Office of Science User Facility.Google Scholar