Search Results for author: Andreas Weinmann

Found 13 papers, 1 papers with code

Low-Dose CT Image Reconstruction by Fine-Tuning a UNet Pretrained for Gaussian Denoising for the Downstream Task of Image Enhancement

no code implementations6 Mar 2024 Tim Selig, Thomas März, Martin Storath, Andreas Weinmann

The crucial point of our approach is that the neural network is pretrained on a distinctly different pretraining task with non-CT data, namely Gaussian noise removal on a variety of natural grayscale images (photographs).

Computed Tomography (CT) Denoising +4

Multi-Channel Potts-Based Reconstruction for Multi-Spectral Computed Tomography

no code implementations12 Sep 2020 Lukas Kiefer, Stefania Petra, Martin Storath, Andreas Weinmann

We consider reconstructing multi-channel images from measurements performed by photon-counting and energy-discriminating detectors in the setting of multi-spectral X-ray computed tomography (CT).

Computed Tomography (CT)

Iterative Potts minimization for the recovery of signals with discontinuities from indirect measurements -- the multivariate case

no code implementations3 Dec 2018 Lukas Kiefer, Martin Storath, Andreas Weinmann

A frequent task is to find the segments of the signal or image which corresponds to finding the discontinuities or jumps in the data.

Electrical Engineering

Wavelet Sparse Regularization for Manifold-Valued Data

no code implementations1 Aug 2018 Martin Storath, Andreas Weinmann

In this paper, we consider the sparse regularization of manifold-valued data with respect to an interpolatory wavelet/multiscale transform.

Variational Regularization of Inverse Problems for Manifold-Valued Data

no code implementations27 Apr 2018 Martin Storath, Andreas Weinmann

In this paper, we consider the variational regularization of manifold-valued data in the inverse problems setting.

Fast Piecewise-Affine Motion Estimation Without Segmentation

no code implementations6 Feb 2018 Denis Fortun, Martin Storath, Dennis Rickert, Andreas Weinmann, Michael Unser

Current algorithmic approaches for piecewise affine motion estimation are based on alternating motion segmentation and estimation.

Motion Estimation Motion Segmentation +1

Model-based learning of local image features for unsupervised texture segmentation

no code implementations1 Aug 2017 Martin Kiechle, Martin Storath, Andreas Weinmann, Martin Kleinsteuber

We note that the features can be learned from a small set of images, from a single image, or even from image patches.

Segmentation

A Second Order Non-Smooth Variational Model for Restoring Manifold-Valued Images

1 code implementation8 Jun 2015 Miroslav Bačák, Ronny Bergmann, Gabriele Steidl, Andreas Weinmann

We introduce a new non-smooth variational model for the restoration of manifold-valued data which includes second order differences in the regularization term.

Numerical Analysis 65K10, 49Q99, 49M37

Total Variation Regularization of Shape Signals

no code implementations CVPR 2015 Maximilian Baust, Laurent Demaret, Martin Storath, Nassir Navab, Andreas Weinmann

This paper introduces the concept of shape signals, i. e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals.

Mumford-Shah and Potts Regularization for Manifold-Valued Data with Applications to DTI and Q-Ball Imaging

no code implementations7 Oct 2014 Andreas Weinmann, Laurent Demaret, Martin Storath

For the multivariate Mumford-Shah and Potts problems (for image regularization) we propose a splitting into suitable subproblems which we can solve exactly using the techniques developed for the corresponding univariate problems.

Denoising

Total variation regularization for manifold-valued data

no code implementations30 Dec 2013 Andreas Weinmann, Laurent Demaret, Martin Storath

For the class of Cartan-Hadamard manifolds (which includes the data space in diffusion tensor imaging) we show the convergence of the proposed TV minimizing algorithms to a global minimizer.

Denoising

Cannot find the paper you are looking for? You can Submit a new open access paper.