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Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).ORCID iD: 0000-0002-1118-6483
2021 (English)In: 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021, Springer Science and Business Media Deutschland GmbH , 2021, p. 540-552Conference paper, Published paper (Refereed)
Abstract [en]

In numerous practical applications, especially in medical image reconstruction, it is often infeasible to obtain a large ensemble of ground-truth/measurement pairs for supervised learning. Therefore, it is imperative to develop unsupervised learning protocols that are competitive with supervised approaches in performance. Motivated by the maximum-likelihood principle, we propose an unsupervised learning framework for solving ill-posed inverse problems. Instead of seeking pixel-wise proximity between the reconstructed and the ground-truth images, the proposed approach learns an iterative reconstruction network whose output matches the ground-truth in distribution. Considering tomographic reconstruction as an application, we demonstrate that the proposed unsupervised approach not only performs on par with its supervised variant in terms of objective quality measures, but also successfully circumvents the issue of over-smoothing that supervised approaches tend to suffer from. The improvement in reconstruction quality comes at the expense of higher training complexity, but, once trained, the reconstruction time remains the same as its supervised counterpart. 

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2021. p. 540-552
Keywords [en]
Generative adversarial networks (GANs), Inverse problems, Iterative reconstruction, Unsupervised learning, Computer vision, Differential equations, Image reconstruction, Iterative methods, Maximum likelihood, Medical imaging, ILL-posed inverse problem, Maximum likelihood Principle, Objective quality measures, Reconstruction quality, Tomographic reconstruction, Training complexity, Unsupervised approaches
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-309651DOI: 10.1007/978-3-030-75549-2_43Scopus ID: 2-s2.0-85106403188OAI: oai:DiVA.org:kth-309651DiVA, id: diva2:1643170
Conference
16 May 2021 through 20 May 2021
Note

Part of proceedings: ISBN 978-3-030-75548-5

QC 20220309

Available from: 2022-03-09 Created: 2022-03-09 Last updated: 2023-01-18Bibliographically approved

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Öktem, Ozan

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf