Deep Learning Techniques for Inverse Problems in Imaging

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  • Additional Information
    • Publication Information:
      IEEE
    • Publication Date:
      2020
    • Abstract:
      Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the tradeoffs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.
    • Author Affiliations:
      Department of Statistics, University of Chicago, Chicago, IL, USA
      Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA
      Department of Electrical Engineering, Stanford University, Stanford, CA, USA
      Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
      Department of Statistics and Computer Science, University of Chicago, Chicago, IL, USA
    • ISSN:
      2641-8770
    • Accession Number:
      10.1109/JSAIT.2020.2991563
    • Rights:
      Copyright 2020, IEEE
    • AMSID:
      9084378
    • Date of Current Version:
      2020
    • Document Subtype:
      IEEE Journal
    • Accession Number:
      edseee.9084378
  • Citations
    • ABNT:
      ONGIE, G. et al. Deep Learning Techniques for Inverse Problems in Imaging. IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J. Sel. Areas Inf. Theory, [s. l.], v. 1, n. 1, p. 39–56, 2020. DOI 10.1109/JSAIT.2020.2991563. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.9084378. Acesso em: 14 ago. 2020.
    • AMA:
      Ongie G, Jalal A, Metzler CA, Baraniuk RG, Dimakis AG, Willett R. Deep Learning Techniques for Inverse Problems in Imaging. IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J Sel Areas Inf Theory. 2020;1(1):39-56. doi:10.1109/JSAIT.2020.2991563
    • APA:
      Ongie, G., Jalal, A., Metzler, C. A., Baraniuk, R. G., Dimakis, A. G., & Willett, R. (2020). Deep Learning Techniques for Inverse Problems in Imaging. IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J. Sel. Areas Inf. Theory, 1(1), 39–56. https://doi.org/10.1109/JSAIT.2020.2991563
    • Chicago/Turabian: Author-Date:
      Ongie, G., A. Jalal, C.A. Metzler, R.G. Baraniuk, A.G. Dimakis, and R. Willett. 2020. “Deep Learning Techniques for Inverse Problems in Imaging.” IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J. Sel. Areas Inf. Theory 1 (1): 39–56. doi:10.1109/JSAIT.2020.2991563.
    • Harvard:
      Ongie, G. et al. (2020) ‘Deep Learning Techniques for Inverse Problems in Imaging’, IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J. Sel. Areas Inf. Theory, 1(1), pp. 39–56. doi: 10.1109/JSAIT.2020.2991563.
    • Harvard: Australian:
      Ongie, G, Jalal, A, Metzler, CA, Baraniuk, RG, Dimakis, AG & Willett, R 2020, ‘Deep Learning Techniques for Inverse Problems in Imaging’, IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J. Sel. Areas Inf. Theory, vol. 1, no. 1, pp. 39–56, viewed 14 August 2020, .
    • MLA:
      Ongie, G., et al. “Deep Learning Techniques for Inverse Problems in Imaging.” IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J. Sel. Areas Inf. Theory, vol. 1, no. 1, May 2020, pp. 39–56. EBSCOhost, doi:10.1109/JSAIT.2020.2991563.
    • Chicago/Turabian: Humanities:
      Ongie, G., A. Jalal, C.A. Metzler, R.G. Baraniuk, A.G. Dimakis, and R. Willett. “Deep Learning Techniques for Inverse Problems in Imaging.” IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J. Sel. Areas Inf. Theory 1, no. 1 (May 1, 2020): 39–56. doi:10.1109/JSAIT.2020.2991563.
    • Vancouver/ICMJE:
      Ongie G, Jalal A, Metzler CA, Baraniuk RG, Dimakis AG, Willett R. Deep Learning Techniques for Inverse Problems in Imaging. IEEE Journal on Selected Areas in Information Theory, Selected Areas in Information Theory, IEEE Journal on, IEEE J Sel Areas Inf Theory [Internet]. 2020 May 1 [cited 2020 Aug 14];1(1):39–56. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.9084378