45 citations · 166 across the 20 of their papers we have counts for
7 papers · 1 filter
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging
Dominik Narnhofer, Andreas Habring, Martin Holler +1
In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced. The proposed method employs estimates of the post…
Unsupervised energy disaggregation via convolutional sparse coding
Christian Aarset, Andreas Habring, Martin Holler +1
In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active…
Exact reconstruction and reconstruction from noisy data with anisotropic total variation
Martin Holler, Benedikt Wirth
It is well-known that point sources with sufficient mutual distance can be reconstructed exactly from finitely many Fourier measurements by solving a convex optimization problem wi…
Latent-space disentanglement with untrained generator networks for the isolation of different motion types in video data
Abdullah Abdullah, Martin Holler, Karl Kunisch +1
Isolating different types of motion in video data is a highly relevant problem in video analysis. Applications can be found, for example, in dynamic medical or biological imaging,…
A Note on the Regularity of Images Generated by Convolutional Neural Networks
Andreas Habring, Martin Holler
The regularity of images generated by convolutional neural networks, such as the U-net, generative networks, or the deep image prior, is analyzed. In a resolution-independent, infi…
Learning-informed parameter identification in nonlinear time-dependent PDEs
Christian Aarset, Martin Holler, Tram Thi Ngoc Nguyen
We introduce and analyze a method of learning-informed parameter identification for partial differential equations (PDEs) in an all-at-once framework. The underlying PDE model is f…
Fast and memory-efficient reconstruction of sparse Poisson data in listmode with non-smooth priors with application to time-of-flight PET
Georg Schramm, Martin Holler
Complete time of flight (TOF) sinograms of state-of-the-art TOF PET scanners have a large memory footprint. Currently, they contain ~4e9 data bins which amount to ~17GB in 32bit fl…