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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings
EditorsAbderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon
PublisherSpringer Science and Business Media Deutschland GmbH
Pages491-502
Number of pages12
ISBN (Print)9783030755485
DOIs
Publication statusPublished - 2021
Event8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021 - Virtual, Online
Duration: 16 May 202120 May 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12679 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021
CityVirtual, Online
Period16/05/2120/05/21

Keywords

  • Deep learning
  • Exit wave reconstruction
  • Nonlinear inverse problem
  • Total deep variation
  • Transmission electron microscopy

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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