Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

Over the last decades, the total variation (TV) has evolved to be one of the most broadly-
used regularisation functionals for inverse problems, in particular for imaging applications …

While current state of the art MR-PET scanners enable simultaneous MR and PET
measurements, the acquired data sets are still usually reconstructed separately. We propose …

We propose a new algorithmic approach to the non-smooth and non-convex Potts problem
(also called piecewise-constant Mumford–Shah problem) for inverse imaging problems. We …

Purpose To accelerate dynamic MR applications using infimal convolution of total
generalized variation functionals (ICTGV) as spatio‐temporal regularization for image …

The aim of this paper is to derive and analyze a variational model for the joint estimation of
motion and reconstruction of image sequences, which is based on a time-continuous …

In multi-modal electron tomography, tilt series of several signals such as X-ray spectra,
electron energy-loss spectra, annular dark-field, or bright-field data are acquired at the same …

A variational model for image reconstruction is introduced and analyzed in function space.
Specific to the model is the data fidelity, which is realized via a basis transformation with …

In this paper, we characterize the class of extremal points of the unit ball of the Hessian–
Schatten total variation (HTV) functional. The underlying motivation for our work stems from …

We study the qualitative properties of optimal regularisation parameters in variational
models for image restoration. The parameters are solutions of bilevel optimisation problems …