direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

There is no English translation for this web page.

Johannes Hertrich


Technische Universität Berlin
Institut für Mathematik
Sekretariat MA 4-3
Straße des 17. Juni 136
10623 Berlin

Raum MA 477
Tel.: +49 - (0)30 - 314-79758
Email: Profiles: Google Scholar, Github


Sekretariat MA 4-3
Julia Wilton
Raum MA 476


F. Altekrüger, A. Denker, P. Hagemann, J. Hertrich, P. Maass and G. Steidl (2022).
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization.
(arXiv Preprint#2205.12021)
[arxiv], [Code]

D.P.L. Nguyen, J. Hertrich, J.-F. Aujol and Y. Berthoumieu (2022).
Image super-resolution with PCA reduced generalized Gaussian mixture models.
(HAL Preprint#hal-03664839)

J. Hertrich and G. Steidl (2022).
Inertial Stochastic PALM and Applications in Machine Learning.
Sampling Theory, Signal Processing, and Data Analysis, vol. 20, no. 4.
[doi], [arxiv], [Code]

F. Altekrüger and J. Hertrich (2022).
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution.
(arXiv Preprint#2201.08157)
[arxiv], [Code]

J. Hertrich, D.P.L. Nguyen, J.-F. Aujol, D. Bernard, Y. Berthoumieu, A. Saadaldin and G. Steidl (2022).
PCA reduced Gaussian mixture models with application in superresolution.
Inverse Problems and Imaging, vol. 16, pp. 341-366.
[doi], [arxiv], [Code]

J. Hertrich, F. Ba and G. Steidl (2022).
Sparse Mixture Models Inspired by ANOVA Decompositions.
Electronic Transactions on Numerical Analysis, vol. 55, pp. 142-168.
[doi], [arxiv], [Code]

P. Hagemann, J. Hertrich and G. Steidl (2021).
Generalized Normalizing Flows via Markov Chains.
(arXiv Preprint#2111.12506)
[arxiv], [Code]

J. Hertrich, A. Houdard and C. Redenbach (2021).
Wasserstein Patch Prior for Image Superresolution.
(arXiv Preprint#2109.12880)
[arxiv], [Code]

P. Hagemann, J. Hertrich and G. Steidl (2021).
Stochastic Normalizing Flows: a Markov Chains Viewpoint.
Accepted in: SIAM/ASA Journal on Uncertainty Quantification.
[arxiv], [Code]

J. Hertrich, S. Neumayer and G. Steidl (2021).
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms.
Linear Algebra and its Applications, vol 631, pp. 203-234.
[doi], [arxiv], [Code]

M. Hasannasab, J. Hertrich, F. Laus and G. Steidl (2021).
Alternatives to the EM Algorithm for ML-Estimation of Location, Scatter Matrix and Degree of Freedom of the Student-t Distribution.
Numerical Algorithms, vol. 87, pp. 77-118.
[doi], [arxiv], [Code]

T. Batard, J. Hertrich and G. Steidl (2020).
Variational models for color image correction inspired by visual perception and neuroscience.
Journal of Mathematical Imaging and Vision, vol. 62, pp. 1173-1194.
[doi], [hal]

M. Hasannasab, J. Hertrich, S.Neumayer, G. Plonka, S. Setzer and G. Steidl (2020).
Parseval Proximal Neural Networks.
Journal of Fourier Analysis and Applications, vol. 26, no. 59.
[doi], [arxiv], [Code]

M. Bačák, J. Hertrich, S. Neumayer and G. Steidl (2020).
Minimal Lipschitz and ∞-Harmonic Extensions of Vector-Valued Functions on Finite Graphs.
Information and Inference: A Journal of the IMA, vol. 9, pp. 935–959.
[doi], [arxiv], [Code]

J. Hertrich, M. Bačák, S. Neumayer, G. Steidl (2019).
Minimal Lipschitz extensions for vector-valued functions on finite graphs.
M. Burger, J. Lellmann and J. Modersitzki (eds.)
Scale Space and Variational Methods in Computer Vision.
Lecture Notes in Computer Science, 11603, 183-195.
[doi], [Code]


Superresolution via Student-t Mixture Models
Master Thesis, 2020
TU Kaiserslautern

Infinity Laplacians on Scalar- and Vector-valued Functions and Optimal Lipschitz Extensions on Graphs
Bachelor Thesis, 2018
TU Kaiserslautern

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

This site uses Matomo for anonymized webanalysis. Visit Data Privacy for more information and opt-out options.