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Comparison Between Deep Learning and Iterative Bayesian Statistics Deconvolution Methods in Energy Electron Loss Spectroscopy

Published online by Cambridge University Press:  22 July 2022

S. Shayan Mousavi M. ,
Alexandre Pofelski  and
Gianluigi A. Botton
S. Shayan Mousavi M.
Affiliation:
McMaster University, Department of Materials Science and Engineering, Hamilton, ON, Canada
Alexandre Pofelski
Affiliation:
McMaster University, Department of Materials Science and Engineering, Hamilton, ON, Canada
Gianluigi A. Botton*
Affiliation:
McMaster University, Department of Materials Science and Engineering, Hamilton, ON, Canada Canadian Light Source, Saskatoon, SK, Canada
*
*Corresponding author: gbotton@mcmaster.ca

Abstract

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Type
On Demand - Artificial Intelligence, Instrument Automation, and High-Dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3154 - 3156
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Copyright
Copyright © Microscopy Society of America 2022

References

Pofelski, Alexandre, and Botton, Gianluigi. "EELSpecNet: Deep Convolutional Neural Network Solution for Electron Energy Loss Spectroscopy Deconvolution." Microscopy and Microanalysis 27.S1 (2021): 1626-1627.Google Scholar
Bellido, Edson P., Rossouw, David, and Botton, Gianluigi A.. "Toward 10 meV electron energy-loss spectroscopy resolution for plasmonics." Microscopy and Microanalysis 20.3 (2014): 767-778.10.1017/S1431927614000609CrossRefGoogle ScholarPubMed
Wang, Zhou, and Bovik, Alan C.. "Mean squared error: Love it or leave it? A new look at signal fidelity measures." IEEE signal processing magazine 26.1 (2009): 98-117.Google Scholar
Wang, Zhou, et al. "Image quality assessment: from error visibility to structural similarity." IEEE transactions on image processing 13.4 (2004): 600-612.10.1109/TIP.2003.819861CrossRefGoogle ScholarPubMed
We are grateful to the Natural Sciences and Engineering Research Council for supporting this work.Google Scholar