16 citations · 19 across the 5 of their papers we have counts for
6 papers
Unified Models for Second-Order TV-Type Regularisation in Imaging - A New Perspective Based on Vector Operators
Eva-Maria Brinkmann, Martin Burger, Joana Sarah Grah
We introduce a novel regulariser based on the natural vector field operations gradient, divergence, curl and shear. For suitable choices of the weighting parameters contained in ou…
Dynamic MRI Reconstruction from Undersampled Data with an Anatomical Prescan
Julian Rasch, Ville Kolehmainen, Riikka Nivajärvi +4
The goal of dynamic magnetic resonance imaging (dynamic MRI) is to visualize tissue properties and their local changes over time that are traceable in the MR signal. We propose a n…
Joint Reconstruction via Coupled Bregman Iterations with Applications to PET-MR Imaging
Julian Rasch, Eva-Maria Brinkmann, Martin Burger
Joint reconstruction has recently attracted a lot of attention, especially in the field of medical multi-modality imaging such as PET-MRI. Most of the developed methods rely on the…
Bias-Reduction in Variational Regularization
Eva-Maria Brinkmann, Martin Burger, Julian Rasch +1
The aim of this paper is to introduce and study a two-step debiasing method for variational regularization. After solving the standard variational problem, the key idea is to add a…
Regularization with Sparse Vector Fields: From Image Compression to TV-type Reconstruction
Eva-Maria Brinkmann, Martin Burger, Joana Grah
This paper introduces a novel variational approach for image compression motivated by recent PDE-based approaches combining edge detection and Laplacian inpainting. The essential f…
Color Bregman TV
Michael Moeller, Eva-Maria Brinkmann, Martin Burger +1
In this paper we present a novel iterative procedure for multichannel image and data reconstruction using Bregman distances. With the motivation that multiple channels sharing a co…