Imaging science studies the acquisition, analysis, processing, transmission, and compression of images. The importance of images among all types of data originates from the fact that images are visually easy to perceive and hence provide a significant percentage of generated data and daily data traffic.
Interestingly, various methodological approaches have been developed and also sometimes combined, such as methods from applied harmonic analysis , pde based methods, variational methods, or topological methods. Moreover, the problems imaging science is concerned with are constantly increasing due to novel imaging technologies with each posing new challenges.
Classical problems are efficient image acquisition, denoising, deblurring, inpainting, and compression. However, as just mentioned, in particular, the analysis tasks are vast, ranging from detecting of different types of cancer alongside with a classification, over measuring diffusivity in diffusion tensor imaging, to denoising in extremely high noise regimes such as in cryo-EM.
Some of our Research Topics
- Development and theoretical analysis of methods for various imaging problems such as inpainting or feature extraction by using methods from applied harmonic analysis  combined with sparse approximation  techniques.
- Applications  to real-world problems such as magnetic resonance imaging.