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Denoising Large In Situ TEM Image Datasets: A Convolutional Neural Network-based Approach

Published online by Cambridge University Press:  30 July 2020

Joshua Vincent
Affiliation:
Arizona State University, Tempe, Arizona, United States
Sreyas Mohan
Affiliation:
New York University, New York City, New York, United States
Carlos Fernandez-Granda
Affiliation:
New York University, New York City, New York, United States
Peter Crozier
Affiliation:
Arizona State University, Tempe, Arizona, United States

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Copyright
Copyright © Microscopy Society of America 2020

References

Somorjai, G. A., Langmuir, 1991. 7(12): p. 3176-3182.10.1021/la00060a043CrossRefGoogle Scholar
Crozier, P. A., et al. ., Microscopy and Microanalysis, 2019. 25(S2): p. 1464-1465.10.1017/S1431927619008055CrossRefGoogle Scholar
Faruqi, A. and McMullan, G., Nucl. Instrum. Methods Phys. Res, 2018. 878: p. 180-190.10.1016/j.nima.2017.07.037CrossRefGoogle Scholar
Barthel, J., Ultramicroscopy, 2018. 193, p. 1-11.10.1016/j.ultramic.2018.06.003CrossRefGoogle Scholar
Mohan, S., Kadkhodaie, Z., Simocelli, E. P., and Fernandez-Granda, C., 2019. Robust and Interpretable Blind Image Denoising via Bias-Free Convolutional Neural Networks, preprint available at https://arxiv.org/abs/1906.05478Google Scholar
We gratefully acknowledge support of NSF grants CBET-1604971 and OAC-1940263, and the facilities at ASU's John M. Cowley Center for High Resolution Electron Microscopy.Google Scholar