Judith Nickel

Zeitschriftenartikel

M. Beckmann, J. Nickel.
Optimized filter functions for filtered back projection reconstructions.
Inverse Problems and Imaging, 2025.
DOI: 10.3934/ipi.2025003

C. Arndt, S. Dittmer, N. Heilenk?tter, M. Iske, T. Kluth, J. Nickel.
Bayesian view on the training of invertible residual networks for solving linear inverse problems.
Inverse Problems, 40 045021 40(4), IOP Science, 2024.
DOI: 10.1088/1361-6420/ad2aaa

C. Arndt, A. Denker, S. Dittmer, N. Heilenk?tter, M. Iske, T. Kluth, P. Maa?, J. Nickel.
Invertible residual networks in the context of regularization theory for linear inverse problems.
Inverse Problems, 39(12), 2023.
DOI: 10.1088/1361-6420/ad0660

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

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

M. Beckmann, P. Maa?, J. Nickel.
Error analysis for filtered back projection reconstructions in Besov spaces.
Inverse Problems, 37, IOP Science, 2020.
DOI: https://doi.org/10.1088/1361-6420/aba5ee

Preprint

C. Arndt, J. Nickel.
Invertible ResNets for inverse imaging problems: Competitive performance with provable regularization properties.
Zur Ver?ffentlichung eingereicht.
online unter: https://arxiv.org/abs/2409.13482