Structural adaptation - a statistical concept for image denoising

Images are often characterized by their homogeneity structure,i.e., discontinuities and smoothness within homogeneous regions, and intensity distributions that depend on the image generating experiment. Structural adaptation employs such qualitative assumptions on the homogeneitystructure in a sequential multi-scale procedure that controls localbias and variance while implicitly recovering discontinuities. I'll discuss the basic principles of the procedure, the model dependent but data-independent selection of it's parameters by a propagation condition and its mainproperties. Generalizations include patch based procedures and methods for noise quantification.I'll use examples from 2D and 3D imaging, aswell as from diffusion MR (5D) and quantitative MR (multiple 3D)for illustration.