Publications of Prof. Dr. Bangti Jin
Articles (15)
- 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 Imagingonline at: https://arxiv.org/abs/2308.14190
- 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 at: https://openreview.net/forum?id=FWyabz82fH
- 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. - 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 at: https://journal.iwmpi.org/index.php/iwmpi/article/view/146 - 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. - T. Kluth, B. Jin, G. Li.
On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging.
Inverse Problems, 34(9), 2018. - 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. - 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
- 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
- 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. - 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. - B. Jin, P. Maaß.
Sparsity regularization for parameter identification problems.
Inverse Problems, 123001 28(12), IOPscience, 2012.Ausgezeichnet als Highlight Paper
- 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. - 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 at: http://arxiv.org/abs/1001.5346 - B. Jin, D. Lorenz, S. Schiffler.
Elastic-Net Regularization: Error estimates and Active Set Methods.
Inverse Problems, 25(11), 2009.
Preprints (5)
- 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 at: https://arxiv.org/abs/2310.18636
- 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 at: https://arxiv.org/abs/2302.10279
- 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 at: https://arxiv.org/abs/2207.05714
- T. Kluth, B. Jin.
Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging.
Zur Veröffentlichung eingereicht. - B. Jin, T. Khan, P. Maaß.
Sparse Reconstruction in Electrical Impedance tomography.
Zur Veröffentlichung eingereicht.
Proceedings (2)
- 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 at: https://2023.midl.io/papers/p014
- 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, pp. 127-128, Infinite Science Publishing, 2018.