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Publikationen von Dr. Daniel Otero Baguer

Zeitschriftenartikel (2)

  1. D. Otero Baguer, J. Leuschner, M. Schmidt.
    Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
    Inverse Problems, 36(9), IOPscience, 2020.

    DOI: https://doi.org/10.1088/1361-6420/aba415

  2. S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
    Regularization by architecture: A deep prior approach for inverse problems.
    Journal of Mathematical Imaging and Vision, :456-470, Springer Verlag, 2020.

    DOI: 10.1007/s10851-019-00923-x
    online unter: http://link.springer.com/article/10.1007/s10851-019-00923-x

Preprints (1)

  1. J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
    The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods.
    Zur Veröffentlichung eingereicht.

    online unter: arXiv:1910.01113

Qualifikationsarbeiten (1)

  1. D. Otero Baguer.
    Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging. (submitted).
    Dissertationsschrift, Universität Bremen, 2020.

Tagungsbeiträge (3)

  1. S. Dittmer, T. Kluth, D. Otero Baguer, B. Maass.
    A Deep Prior Approach to Magnetic Particle Imaging.
    Machine Learning for Medical Image Reconstruction 2020.
    Springer International Publishing, F. Deeba, P. Johnson, T. Würfl, J. C. Ye (Hrsg.), S. 113-122, 2020.
  2. T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maaß.
    Connection Between Shock Wave Induced Indentations And Hardness By Means Of Neural Networks.
    22nd International Conference on Material Forming (ESAFORM 2019), 08.05.-10.05.2019.
    AIP Conference Proceedings 2113, 100001, Springer Verlag, 2019.

    DOI: 10.1063/1.5112634

  3. D. Otero Baguer, I. Piotrowska, P. Maaß.
    Inverse Problems in designing new structural materials.
    7th International Conference on High Performance Scientific Computing, 19.03-23.03.2018, Hanoi, Vietnam.

    DOI: 10.1007/978-3-030-55240-4_8