activity
20172022
most citedEfficient regularization with wavelet sparsity constraints in PAT

2 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

eess.IV20221 cited

Feature reconstruction from incomplete tomographic data without detour

Simon Göppel, Jürgen Frikel, Markus Haltmeier

In this paper, we consider the problem of feature reconstruction from incomplete x-ray CT data. Such problems occurs, e.g., as a result of dose reduction in the context medical ima…

math.NA2021

Combining reconstruction and edge detection in computed tomography

Jürgen Frikel, Simon Göppel, Markus Haltmeier

We present two methods that combine image reconstruction and edge detection in computed tomography (CT) scans. Our first method is as an extension of the prominent filtered backpro…

math.NA2020

A new 3D model for magnetic particle imaging using realistic magnetic field topologies for algebraic reconstruction

Gaël Bringout, Wolfgang Erb, Jürgen Frikel

We derive a new 3D model for magnetic particle imaging (MPI) that is able to incorporate realistic magnetic fields in the reconstruction process. In real MPI scanners, the generate…

math.NA2019

Sparse regularization of inverse problems by operator-adapted frame thresholding

Jürgen Frikel, Markus Haltmeier

We analyze sparse frame based regularization of inverse problems by means of a diagonal frame decomposition (DFD) for the forward operator, which generalizes the SVD. The DFD allow…

math.OC20172 cited

Efficient regularization with wavelet sparsity constraints in PAT

Jürgen Frikel, Markus Haltmeier

In this paper we consider the reconstruction problem of photoacoustic tomography (PAT) with a flat observation surface. We develop a direct reconstruction method that employs regul…