Speaker
Description
Variational methods are powerful tools in image processing.
Basically we are searching for a suitable mathematical model (function) consisting of a data term and a prior
which minimizer provides a solution of the task at hand and can be computed in an efficient, reliable way. Typically this leads to non-smooth, high-dimensional
optimization problems.
This talk deals with recent results obtained by applying
variational methods for different tasks in material sciences as
- crack detection using optical flow models in image sequences,
- determination of deformation fields in electron backscatter diffraction image sequences,
- superresolution of material images by learned patch-based priors,
and
- denoising of FIB images with directional total variation priors.
