The talk presents a class of structural adaptive smoothing methods developed at WIAS.  The main focus will be on the 
Propagation-Separation (PS) approach proposed in Polzehl and Spokoiny (2006).  The method allows to simultaneously 
identify regions of homogeneity with respect to a prescribed model (structural assumption) and to use this information 
to improve local estimates. This is achieved by an iterative procedure.  The name Propagation-Separation is a synonym 
for the two main properties of the algorithms. In case of homogeneity, that is if the prescribed model holds with the 
same parameters within a large region, the algorithm essentially delivers a series of nonadaptive estimates with 
decreasing variance and propagates to the best estimate from this series.  Separation means that, as soon as in two 
design points i and j significant differences are detected between estimates, observations in j will not be used to
estimate the parameter in j. We establish some theoretical {nonasymptotic} results on properties of the new algorithm.
The power of the approach will be demonstrated for several imaging problems. This includes denoising of greyvalue and 
color images, demosaicing and adaptation to anisotropic structures in images.  I'll also present how these methods can 
be applied in the analysis of functional Magnetic Resonance Imaging experiments and Diffusion Tensor Imaging data.