I am now leading the Computational Imaging Group in the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen.
I am interested in a wide range of signal and image processing problems in scientific imaging as well as computational photography. My particular interest is on physical modeling and the development of efficient and novel inference schemes for inverse problems.
Please do visit our group page for more information on our research.
In Proceedings of the First IEEE International Conference Computational Photography (ICCP 2009), pages: 1-7, IEEE, Piscataway, NJ, USA, First IEEE International Conference on Computational Photography (ICCP), April 2009 (inproceedings)
Atmospheric turbulences blur astronomical images taken by earth-based telescopes. Taking many short-time exposures in such a situation provides noisy images of the same object, where each noisy image has a different blur. Commonly astronomers apply a technique called “Lucky Imaging” that selects a few of the recorded frames that fulfill certain criteria, such as reaching a certain peak intensity (“Strehl ratio”). The selected frames are then averaged to obtain a better image. In this paper we introduce and analyze a new method that exploits all the frames and generates an improved image in an online fashion. Our initial experiments with controlled artificial data and real-world astronomical datasets yields promising results.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems