Felix Lucka

welcome to my academic web site

Majorization-minimization from a hierarchical Bayesian perspective

A while ago, I got involved in an interesting collaboration with Alexandre Gramfort, Joseph Salmon & Yousra Bekhti from the Télécom ParisTech. We drew a connection between majorization-minimization methods for sparse non-convex regression and a particular kind of hierarchical Bayesian modeling. Furthermore, we demonstrate how it can be used to quantify the inherent uncertainty and ambiguity of ill-posed regression problems such as EEG/MEG source reconstruction. The paper can be found on arXiv, big thanks to all the co-authors!

Simultaneous Motion Estimation for CS-PAT

We just submitted our second major paper on how to improve dynamic, high-resolution 3D photoacoustic tomography (4D PAT) by novel imaging techniques. It combines compressed sensing data acquisition with a generic spatio-temporal regularization framework that incorporates a PDE-based motion model. The paper can be found on arXiv, big thanks to all the co-authors!

New job at the Centrum Wiskunde & Informatica

I just moved to Amsterdam to pick up a tenure track position at the Centrum Wiskunde & Informatica (CWI). With the group for Computational Imaging, headed by Joost Batenburg, I will work on dynamic computerized tomography (CT). Their facilities include a world class X-ray lab that allows you to design a wide range of interesting challenging experiments. Check out the news story about the opening of the lab including a pretty cool animation

Iterative image reconstruction and deep learning

Blending deep learning and iterative image reconstruction has shown great promise to obtain high quality reconstructions from noisy, sub-sampled data and is therefore hot topic in inverse problems at the moment. We adopted an particular approach to enhance the reconstruction of blood vessel structures from sub-sampled, limited-view 3D photoacoustic tomography (PAT) in vivo. Many thanks to Andreas Hauptmann, who did the main work for this exciting project. The paper with all the results can be found on arXiv.