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SIAMIS
2010

Global Solutions of Variational Models with Convex Regularization

8 years 2 months ago
Global Solutions of Variational Models with Convex Regularization
Abstract. We propose an algorithmic framework for computing global solutions of variational models with convex regularity terms that permit quite arbitrary data terms. While the minimization of variational problems with convex data and regularity terms is straightforward (using, for example, gradient descent), this is no longer trivial for functionals with nonconvex data terms. Using the theoretical framework of calibrations, the original variational problem can be written as the maximum flux of a particular vector field going through the boundary of the subgraph of the unknown function. Upon relaxation this formulation turns the problem into a convex problem, although in a higher dimension. In order to solve this problem, we propose a fast primal-dual algorithm which significantly outperforms existing algorithms. In experimental results we show the application of our method to outlier filtering of range images and disparity estimation in stereo images using a variety of convex regular...
Thomas Pock, Daniel Cremers, Horst Bischof, Antoni
Added 21 May 2011
Updated 21 May 2011
Type Journal
Year 2010
Where SIAMIS
Authors Thomas Pock, Daniel Cremers, Horst Bischof, Antonin Chambolle
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