Alexander Denker

Zeitschriftenartikel

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

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

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

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

Preprints

F. Altenkrüger, A. Denker, P. Hagemann, P. Maaß, G. Steidl.
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2205.12021

Tagungsbeiträge

M. Schmidt, A. Denker, J. Leuschner.
The Deep Capsule Prior - advantages through complexity.
GAMM 92st 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

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