Seminario di analisi numerica
ore
11:00
presso Seminario VIII piano
Image registration is anther important task in image processing, where regularization is a major
issue in designing new models.
The total variation (TV) semi-norm based regularization is much well-known for image denoising and
also useful in registration modelling, with recent work generalised with the help of Bregman distance.
However mean curvature regularization serves as a strong competitor to the
TV. In this talk, I shall first review the mean curvature model by Lysaker-Osher-Tai (2004) and
the related Zhu-Chan (2008,2012) models for image denoising. Then I briefly discuss 2 ways of
speeding up the computational convergence. Finally I show how to use the mean curvature to
minimize the deformation fields in a registration model and highlight the advantages of the
resulting new model.