Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy

Thomas Pinetz; Erich Kobler; Christian Doberstein; Benjamin Berkels; Alexander Effland

doi:10.1007/978-3-030-75549-2_39

Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy

Thomas Pinetz^*, Erich Kobler, Christian Doberstein, Benjamin Berkels, Alexander Effland

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Maschinelles Sehen und Darstellen (7100)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

Transmission electron microscopes (TEMs) are ubiquitous devices for high-resolution imaging on an atomic level. A key problem related to TEMs is the reconstruction of the exit wave, which is the electron signal at the exit plane of the examined specimen. Frequently, this reconstruction is cast as an ill-posed nonlinear inverse problem. In this work, we integrate the data-driven total deep variation regularizer to reconstruct the exit wave in this inverse problem. In several numerical experiments, the applicability of the proposed method is demonstrated for different materials.

Originalsprache	englisch
Titel	Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings
Redakteure/-innen	Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon
Herausgeber (Verlag)	Springer Science and Business Media Deutschland GmbH
Seiten	491-502
Seitenumfang	12
ISBN (Print)	9783030755485
DOIs	https://doi.org/10.1007/978-3-030-75549-2_39
Publikationsstatus	Veröffentlicht - 2021
Veranstaltung	8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021 - Virtual, Online Dauer: 16 Mai 2021 → 20 Mai 2021

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	12679 LNCS
ISSN (Print)	0302-9743
ISSN (elektronisch)	1611-3349

Konferenz

Konferenz	8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021
Ort	Virtual, Online
Zeitraum	16/05/21 → 20/05/21

ASJC Scopus subject areas

Theoretische Informatik
Informatik (insg.)

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10.1007/978-3-030-75549-2_39

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Pinetz, T., Kobler, E., Doberstein, C., Berkels, B., & Effland, A. (2021). Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy. in A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, & L. Simon (Hrsg.), Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings (S. 491-502). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12679 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-75549-2_39

Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy. / Pinetz, Thomas; Kobler, Erich; Doberstein, Christian et al.
Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings. Hrsg. / Abderrahim Elmoataz; Jalal Fadili; Yvain Quéau; Julien Rabin; Loïc Simon. Springer Science and Business Media Deutschland GmbH, 2021. S. 491-502 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12679 LNCS).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Pinetz, T, Kobler, E, Doberstein, C, Berkels, B & Effland, A 2021, Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy. in A Elmoataz, J Fadili, Y Quéau, J Rabin & L Simon (Hrsg.), Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 12679 LNCS, Springer Science and Business Media Deutschland GmbH, S. 491-502, 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021, Virtual, Online, 16/05/21. https://doi.org/10.1007/978-3-030-75549-2_39

Pinetz T, Kobler E, Doberstein C, Berkels B, Effland A. Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy. in Elmoataz A, Fadili J, Quéau Y, Rabin J, Simon L, Hrsg., Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. S. 491-502. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-75549-2_39

Pinetz, Thomas ; Kobler, Erich ; Doberstein, Christian et al. / Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy. Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings. Hrsg. / Abderrahim Elmoataz ; Jalal Fadili ; Yvain Quéau ; Julien Rabin ; Loïc Simon. Springer Science and Business Media Deutschland GmbH, 2021. S. 491-502 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy",

abstract = "Transmission electron microscopes (TEMs) are ubiquitous devices for high-resolution imaging on an atomic level. A key problem related to TEMs is the reconstruction of the exit wave, which is the electron signal at the exit plane of the examined specimen. Frequently, this reconstruction is cast as an ill-posed nonlinear inverse problem. In this work, we integrate the data-driven total deep variation regularizer to reconstruct the exit wave in this inverse problem. In several numerical experiments, the applicability of the proposed method is demonstrated for different materials.",

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author = "Thomas Pinetz and Erich Kobler and Christian Doberstein and Benjamin Berkels and Alexander Effland",

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AU - Berkels, Benjamin

AU - Effland, Alexander

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AB - Transmission electron microscopes (TEMs) are ubiquitous devices for high-resolution imaging on an atomic level. A key problem related to TEMs is the reconstruction of the exit wave, which is the electron signal at the exit plane of the examined specimen. Frequently, this reconstruction is cast as an ill-posed nonlinear inverse problem. In this work, we integrate the data-driven total deep variation regularizer to reconstruct the exit wave in this inverse problem. In several numerical experiments, the applicability of the proposed method is demonstrated for different materials.

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BT - Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings

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A2 - Fadili, Jalal

A2 - Quéau, Yvain

A2 - Rabin, Julien

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Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy

Abstract

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