Johannes Leuschner

Buchkapitel

P. Maaß, S. Dittmer, T. Kluth, J. Leuschner, M. Schmidt.
Mathematische Architekturen für Neuronale Netze.
Erfolgsformeln – Anwendungen der Mathematik, M. Ehrhardt, M. Günther, W. Schilders (Hrsg.), Mathematische Semesterberichte, S. 190-195, Springer Verlag, 2022.
DOI: 10.1007/s00591-022-00325-y

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

S. Schulze, J. Leuschner, E. King.
Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients.
MDPI Open Access Journals Signals, 2(4):637-661, 2021.
DOI: 10.3390/signals2040039.
Online unter: https://www.mdpi.com/2624-6120/2/4/39

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, D. Otero Baguer, P. Maaß.
LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.
Scientific Data, 8(109), 2021.
DOI: 10.1038/s41597-021-00893-z

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

D. Otero Baguer, J. Leuschner, M. Schmidt.
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
Inverse Problems, 36(9), IOPscience, 2020.
DOI: https://doi.org/10.1088/1361-6420/aba415

J. Leuschner, M. Schmidt, P. Fernsel, D. Lachmund, T. Boskamp, P. Maaß.
Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications.
Bioinformatics, bty909 , 2018.
DOI: 10.1093/bioinformatics/bty909

Preprints

T. Lütjen, F. Schönfeld, J. Leuschner, M. Schmidt, A. Wald, T. Kluth.
Learning-based approaches for reconstructions with inexact operators in nanoCTapplications.
Zur Veröffentlichung eingereicht.
online unter: https://aps.arxiv.org/abs/2307.10474

J. Antorán, R. Barbano, J. Leuschner, J. M. Hernández-Lobato, B. Jin.
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2203.00479

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

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

Sonstiges

C. Brandt, M. Hamann, J. Leuschner.
Regression Models for Ultrasonic Testing of Carbon Fiber Reinforced Polymers.
Berichte aus der Technomathematik 19–01, Universität Bremen, 2019.