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Publikationen von Dr. Alexander Denker

Zeitschriftenartikel (8)

  1. F. Altenkrüger, A. Denker, P. Hagemann, P. Maaß, G. Steidl.
    PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
    Inverse Problems, 39(6), 2023.

    online unter: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta

  2. C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
    Invertible residual networks in the context of regularization theory for linear inverse problems.
    Inverse Problems, 39(12), IOPscience, 2023.

    DOI: 10.1088/1361-6420/ad0660
    online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

  3. C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
    Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
    Applied Mathematics for Modern Challenges, 1(2), 2023.

    DOI: 10.3934/ammc.2023007

  4. A. Denker, I. Singh, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maaß, S. Arridge.
    Score-Based Generative Models for PET Image Reconstruction.
    Erscheint in Machine Learning for Biomedical Imaging

    online unter: https://arxiv.org/abs/2308.14190

  5. R. Barbano, J. Leuschner, M. Schmidt, A. Denker, A. Hauptmann, P. Maaß, B. Jin.
    An Educated Warm Start For Deep Image Prior-based Micro CT Reconstruction.
    IEEE Transactions on Computational Imaging, 8:1210-1222, 2022.

    DOI: 10.1109/TCI.2022.3233188

  6. C. Arndt, A. Denker, J. Nickel, J. Leuschner, M. Schmidt, G. Rigaud.
    In Focus - hybrid deep learning approaches to the HDC2021 challenge.
    Inverse Problems and Imaging, , 2022.

    DOI: 10.3934/ipi.2022061

  7. A. Denker, M. Schmidt, J. Leuschner, P. Maaß.
    Conditional Invertible Neural Networks for Medical Imaging .
    MDPI Journal of Imaging, Inverse Problems and Imaging 7(11), 243 S., 2021.

    DOI: 10.3390/jimaging7110243

  8. J. Leuschner, M. Schmidt, P. Ganguly, V. Andriiashen, S. Coban, A. Denker, D. Bauer, A. Hadjifaradji, K. Batenburg, B. Maass, M. von Eijnatten.
    Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
    MDPI Journal of Imaging, 7(3), 44 S., 2021.

    DOI: 10.3390/jimaging7030044
    online unter: https://www.mdpi.com/2313-433X/7/3/44

Tagungsbeiträge (2)

  1. M. Schmidt, A. Denker, J. Leuschner.
    The Deep Capsule Prior - advantages through complexity.
    GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
    Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021.

    DOI: 10.1002/pamm.202100166

  2. A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
    Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
    ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.

    online unter: https://invertibleworkshop.github.io/accepted_papers/index.html