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Publikationen von Prof. Dr. Bangti Jin

Zeitschriftenartikel (15)

  1. 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

  2. J. Antorán, R. Barbano, J. Leuschner, J. M. Hernández-Lobato, B. Jin.
    Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
    Transactions on Machine Learning Research, 12, 2023.

    online unter: https://openreview.net/forum?id=FWyabz82fH

  3. 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

  4. T. Kluth, B. Jin.
    L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset.
    International Journal on Magnetic Particle Imaging, , 2020.

    DOI: 10.18416/IJMPI.2020.2012001
    online unter: https://journal.iwmpi.org/index.php/iwmpi/article/view/146

  5. T. Kluth, B. Jin.
    Enhanced Reconstruction in Magnetic Particle Imaging by Whitening and Randomized SVD Approximation.
    Physics in Medicine and Biology, Article ID 125026 64(12), 2019.

    DOI: 10.1088/1361-6560/ab1a4f

  6. T. Kluth, B. Jin, G. Li.
    On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging.
    Inverse Problems, 34(9), 2018.

    DOI: 10.1088/1361-6420/aad015

  7. V. M. Calo, N. Collier, M. Gehre, B. Jin, H. Radwan, M. Santillana.
    Gradient-based estimation of Manning's friction coefficient from noisy data.
    Journal of Computational and Applied Mathematics, 238:1-13, 2013.

    DOI: 10.1016/j.cam.2012.08.004

  8. B. Jin, T. Khan, P. Maaß.
    A reconstruction algorithm for electrical impedance tomography based on sparsity regularization .
    International Journal for Numerical Methods in Engineering, 89(3):337-353, 2012.

    DOI: 10.1002/nme.3247

  9. B. Jin, P. Maaß.
    An analysis of electrical impedance tomography with applications to Tikhonov regularization.
    ESAIM: Control, Optimisation and Calculus of Variations, 18(4):1027-1048, 2012.

    DOI: 10.1051/cocv/2011193

  10. B. Jacob, B. Jin, T. Khan, P. Maaß.
    Optimal Source for Maximum Distinguishability in Optical Imaging.
    Journal of Applied Functional Analysis, 7(4):394-412, 2012.
  11. M. Gehre, T. Kluth, A. Lipponen, B. Jin, A. Seppänen, J. P. Kaipio, P. Maaß.
    Sparsity Reconstruction in Electrical Impedance Tomography: An Experimental Evaluation.
    Journal of Computational and Applied Mathematics, 236(8):2126-2136, 2012.

    DOI: 10.1016/j.cam.2011.09.035

  12. B. Jin, P. Maaß.
    Sparsity regularization for parameter identification problems.
    Inverse Problems, 123001 28(12), IOPscience, 2012.

    Ausgezeichnet als Highlight Paper

    DOI: 10.1088/0266-5611/28/12/123001

  13. B. Jin, T. Khan, P. Maaß, M. Pidcock.
    Function Spaces and Optimal Currents in Impedance Tomography.
    Journal of Inverse and Ill-posed Problems, 19(1):25-48, 2011.

    DOI: 10.1515/JIIP.2011.022, /May/2011

  14. D. Lorenz, B. Jin.
    Heuristic parameter-choice rules for convex variational regularization based on error estimates.
    SIAM Journal on Numerical Analysis, 48(3):1208-1229, 2010.

    DOI: 10.1137/100784369
    online unter: http://arxiv.org/abs/1001.5346

  15. B. Jin, D. Lorenz, S. Schiffler.
    Elastic-Net Regularization: Error estimates and Active Set Methods.
    Inverse Problems, 25(11), 2009.

    DOI: 10.1088/0266-5611/25/11/115022

Preprints (5)

  1. D. Nganyu Tanyu, J. Ning, A. Hauptmann, B. Jin, P. Maaß.
    Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2310.18636

  2. R. Barbano, J. Antorán, J. Leuschner, J. M. Hernández-Lobato, B. Jin, Z. Kereta.
    Image Reconstruction via Deep Image Prior Subspaces.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2302.10279

  3. R. Barbano, J. Leuschner, J. Antorán, B. Jin, J. M. Hernández-Lobato.
    Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2207.05714

  4. T. Kluth, B. Jin.
    Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging.
    Zur Veröffentlichung eingereicht.
  5. B. Jin, T. Khan, P. Maaß.
    Sparse Reconstruction in Electrical Impedance tomography.
    Zur Veröffentlichung eingereicht.

Tagungsbeiträge (2)

  1. M. Nittscher, M. F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maaß.
    SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
    Medical Imaging with Deep Learning (MIDL 2023), 10.07.-12.07.2023.

    online unter: https://2023.midl.io/papers/p014

  2. T. Kluth, B. Jin.
    Exploiting Ill-Posedness in Magnetic Particle Imaging - System Matrix Approximation via Randomized SVD.
    International Workshop on Magnetic Particle Imaging 2018.
    International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, S. 127-128, Infinite Science Publishing, 2018.