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TU Berlin

Inhalt des Dokuments

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, Orcid


Sekretariat MA 4-3
Julia Wilton
Raum MA 476


J. Hertrich (2022).
Proximal Residual Flows for Bayesian Inverse problems.
(arXiv Preprint#2211.17158)
[arxiv], [Code]

J. Hertrich, M. Gräf, R. Beinert and G. Steidl (2022).
Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels.
(arXiv Preprint#2211.01804)

P. Hagemann, J. Hertrich and G. Steidl (2022).
Stochastic Normalizing Flows: a Markov Chains Viewpoint.
SIAM/ASA Journal on Uncertainty Quantification, vol. 10, pp. 1162-1190.
[doi], [arxiv], [Code]

J. Hertrich, A. Houdard and C. Redenbach (2022).
Wasserstein Patch Prior for Image Superresolution.
IEEE Transactions on Computational Imaging, vol. 8, pp. 693-704.
[doi], [arxiv], [Code]

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.
Accepted in: Elements in Non-local Data Interactions: Foundations and Applications.
Cambridge University Press.
[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

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