Thomas Pinetz
Dr. Thomas Pinetz
  • Institute for Applied Mathematics
Research topics
  • Computer Vision
  • Machine Learning/Deep Learning
Thomas studied Software and Information Engineering and Visual Computing at the Technical University of Vienna with a focus on deep learning in imaging. He then completed a PhD at the Graz University of Technology working on general inverse problems in imaging using learning based techniques. We also used Wasserstein distances to apply learning based models in the absence of ground truth. Thomas initially worked together with Prof. Effland in Graz and is now continuing in this line of research with a focus on medical imaging applications as a post doc.
Selected publications

Thomas Pinetz, Erich Kobler, Thomas Pock and Alexander Effland (2021) Shared prior learning of energy-based models for image reconstruction in SIAM Journal of Imaging Science.

Thomas Pinetz, Erich Kobler, Christian Doberstein, Benjamin Berkels, and Alexander Effland (2021) Total deep variation for noisy exit wave reconstruction in transmission electron microscopy in International Conference on Scale Spaces and Variational Methods in Computer Vision.

Thomas Pinetz, Daniel Soukup and Thomas Pock. (2019) On the estimation of the wasserstein distance in generative models in German Conference on Pattern Recognition.

Thomas Pinetz

N 2.002

Endenicher Allee 60

53115 Bonn

Wird geladen