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Using Deep Learning to Deconvolute Complex Spectra for Hyperspectral Imaging Applications

Published online by Cambridge University Press:  05 August 2019

Samantha Rudinsky ,
Yu Yuan ,
Francis B. Lavoie ,
Raynald Gauvin ,
Ryan Gosselin ,
Nadi Braidy ,
Nicolas Piché  and
Mike Marsh
Samantha Rudinsky
Affiliation:
Department of Materials Engineering, McGill University. Montreal, Canada.
Yu Yuan
Affiliation:
Department of Materials Engineering, McGill University. Montreal, Canada.
Francis B. Lavoie
Affiliation:
Department of Chemical Engineering and Biotechnological Engineering, Université de Sherbrooke. Sherbrooke, Canada.
Raynald Gauvin
Affiliation:
Department of Materials Engineering, McGill University. Montreal, Canada.
Ryan Gosselin
Affiliation:
Department of Chemical Engineering and Biotechnological Engineering, Université de Sherbrooke. Sherbrooke, Canada.
Nadi Braidy
Affiliation:
Department of Chemical Engineering and Biotechnological Engineering, Université de Sherbrooke. Sherbrooke, Canada.
Nicolas Piché
Affiliation:
Object Research Systems. Montreal, Canada.
Mike Marsh
Affiliation:
Object Research Systems. Denver, USA.

Abstract

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Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 178 - 179
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Kotula, P.G., et al. , Microscopy and Microanalysis 9 (2003), p. 1-17.Google Scholar
[2]Piché, N., et al. , Microscopy and Microanalysis 24 (Suppl 1) (2018), p. 560-561.Google Scholar
[3]Gauvin, R., et al. , Microscopy and Microanalysis 15 (Suppl 2) (2009), p. 488-489.Google Scholar