no code implementations • 6 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).
Ranked #1 on Medical Image Enhancement on LoDoPaB-CT
no code implementations • 30 Dec 2023 • Vladyslav Gapyak, Corinna Rentschler, Thomas März, Andreas Weinmann
Magnetic particle imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years.
no code implementations • 19 Apr 2023 • Vanessa Suessle, Mimi Arandjelovic, Ammie K. Kalan, Anthony Agbor, Christophe Boesch, Gregory Brazzola, Tobias Deschner, Paula Dieguez, Anne-Céline Granjon, Hjalmar Kuehl, Anja Landsmann, Juan Lapuente, Nuria Maldonado, Amelia Meier, Zuzana Rockaiova, Erin G. Wessling, Roman M. Wittig, Colleen T. Downs, Andreas Weinmann, Elke Hergenroether
The manual processing and analysis of videos from camera traps is time-consuming and includes several steps, ranging from the filtering of falsely triggered footage to identifying and re-identifying individuals.
no code implementations • 12 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).
no code implementations • 3 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.
no code implementations • 1 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.
no code implementations • 27 Apr 2018 • Martin Storath, Andreas Weinmann
In this paper, we consider the variational regularization of manifold-valued data in the inverse problems setting.
no code implementations • 6 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.
no code implementations • 1 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.
1 code implementation • 8 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
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.
no code implementations • 7 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.
no code implementations • 30 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.