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Electron Image Reconstruction for Pixelated Semiconductor Tracking Detectors Based on Neural Networks

Published online by Cambridge University Press:  22 July 2022

B. Eckert ,
S. Aschauer ,
E. Hedley ,
M. Huth ,
P. Majewski  and
L. Strüder
B. Eckert*
Affiliation:
PNDetector GmbH, München, Germany University of Siegen, Department of Physics, Siegen, Germany
S. Aschauer
Affiliation:
PNDetector GmbH, München, Germany
E. Hedley
Affiliation:
University of Oxford, Department of Materials, Oxford, United Kingdom
M. Huth
Affiliation:
PNDetector GmbH, München, Germany
P. Majewski
Affiliation:
PNDetector GmbH, München, Germany
L. Strüder
Affiliation:
University of Siegen, Department of Physics, Siegen, Germany PNSensor GmbH, München, Germany
*
*Corresponding author: bjoern.eckert@pndetector.de

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3040 - 3042
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Copyright
Copyright © Microscopy Society of America 2022

References

Ronneberger, O. et al. , U-net: Convolutional networks for biomedical image segmentation, Springer, International Conference on Medical image computing and computer-assisted intervention, p. 234-241 (2015)CrossRefGoogle Scholar
Eckert, B. et al. , Electron Imaging Reconstruction for Pixelated Semiconductor Tracking Detectors Using the Approach of Convolutional Neural Networks, submitted to IEEE, Transaction on Nuclear Science (2021)CrossRefGoogle Scholar
International Organization for Standardization, Photography - Electronic Still Picture Imaging-Resolution and Spatial Frequency Responses, ISO 12233:2017, ISO (2017)Google Scholar