J
Johannes Hertrich
Researcher at Technical University of Berlin
Publications - 28
Citations - 123
Johannes Hertrich is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Computer science & Expectation–maximization algorithm. The author has an hindex of 3, co-authored 17 publications receiving 41 citations. Previous affiliations of Johannes Hertrich include Kaiserslautern University of Technology.
Papers
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Journal ArticleDOI
Parseval Proximal Neural Networks
Marzieh Hasannasab,Johannes Hertrich,Sebastian Neumayer,Gerlind Plonka,Simon Setzer,Gabriele Steidl +5 more
TL;DR: The aim of this paper is to show that a certain concatenation of a proximity operator with an affine operator is again a proximity operators on a suitable Hilbert space and establish so-called proximal neural networks (PNNs) and stable tight frame proximal Neural networks.
Journal ArticleDOI
Parseval Proximal Neural Networks
Marzieh Hasannasab,Johannes Hertrich,Sebastian Neumayer,Gerlind Plonka,Simon Setzer,Gabriele Steidl +5 more
TL;DR: In this article, it was shown that a certain concatenation of a proximity operator with an affine operator is again a proximal operator on a suitable Hilbert space, which can be used to establish stable tight frame proximal neural networks.
Posted Content
PCA Reduced Gaussian Mixture Models with Applications in Superresolution
Johannes Hertrich,Dang Phoung Lan Nguyen,Jean-François Aujol,Dominique Bernard,Yannick Berthoumieu,Abdellatif Saadaldin,Gabriele Steidl +6 more
TL;DR: A Gaussian mixture model in conjunction with a reduction of the dimensionality of the data in each component of the model by principal component analysis, which is called PCA-GMM is proposed and applied for the superresolution of 2D and 3D material images.
Journal ArticleDOI
PCA reduced Gaussian mixture models with applications in superresolution
Johannes Hertrich,Paul Bew,Dang Phoung Lan Nguyen,Jean-François Aujol,Dominique Bernard,Yannick Berthoumieu,Abdellatif Saadaldin,Gabriele Steidl +7 more
TL;DR: In this article, a Gaussian mixture model is proposed to reduce the dimensionality of the data in each component of the model by principal component analysis, which is called PCA-GMM.
Journal ArticleDOI
Alternatives to the EM Algorithm for ML-Estimation of Location, Scatter Matrix and Degree of Freedom of the Student-$t$ Distribution
TL;DR: It is proved that under certain assumptions a minimizer of the negative log-likelihood function exists, where there have to take special care of the case $
u \rightarrow \infty$, for which the Student-$t$ distribution approaches the Gaussian distribution.