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Adversarial regularizers in inverse problems
KTH, School of Engineering Sciences (SCI), Centres, Center for Industrial and Applied Mathematics, CIAM. KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).ORCID iD: 0000-0002-1118-6483
2018 (English)In: Advances in Neural Information Processing Systems, Neural information processing systems foundation , 2018, p. 8507-8516Conference paper, Published paper (Refereed)
Abstract [en]

Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods. Among those variational regularization models are one of the most popular approaches. We propose a new framework for applying data-driven approaches to inverse problems, using a neural network as a regularization functional. The network learns to discriminate between the distribution of ground truth images and the distribution of unregularized reconstructions. Once trained, the network is applied to the inverse problem by solving the corresponding variational problem. Unlike other data-based approaches for inverse problems, the algorithm can be applied even if only unsupervised training data is available. Experiments demonstrate the potential of the framework for denoising on the BSDS dataset and for computed tomography reconstruction on the LIDC dataset.

Place, publisher, year, edition, pages
Neural information processing systems foundation , 2018. p. 8507-8516
Keywords [en]
Computerized tomography, Differential equations, Image reconstruction, Medical imaging, Medical problems, Problem solving, Data-driven approach, De-noising, Ground truth, Model-based method, Tomography reconstruction, Unsupervised training, Variational problems, Variational regularization, Inverse problems
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-252271ISI: 000461852003010Scopus ID: 2-s2.0-85064820716OAI: oai:DiVA.org:kth-252271DiVA, id: diva2:1322095
Conference
32nd Conference on Neural Information Processing Systems, NeurIPS 2018, 2 December 2018 through 8 December 2018
Note

QC20190610

Available from: 2019-06-10 Created: 2019-06-10 Last updated: 2022-06-26Bibliographically approved

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Scopushttps://nips.cc/Conferences/2018

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Öktem, Ozan

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CiteExportLink to record
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  • apa
  • ieee
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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Output format
  • html
  • text
  • asciidoc
  • rtf