Name
Abstract | ||
|---|---|---|
We consider total variation (TV) minimization for manifold-valued data. We propose a cyclic proximal point algorithm and a parallel proximal point algorithm to minimize TV functionals with l(p) -type data terms in the manifold case. These algorithms are based on iterative geodesic averaging which makes them easily applicable to a large class of data manifolds. As an application, we consider denoising images which take their values in a manifold. We apply our algorithms to diffusion tensor images and interferometric SAR images as well as sphere-and cylinder-valued images. For the class of Cartan-Hadamard manifolds (which includes the data space in diffusion tensor imaging) we show the convergence of the proposed TV minimizing algorithms to a global minimizer. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1137/130951075 | SIAM JOURNAL ON IMAGING SCIENCES |
Keywords | DocType | Volume |
total variation minimization,manifold-valued data,proximal point algorithm,diffusion tensor imaging | Journal | 7 |
Issue | ISSN | Citations |
4 | 1936-4954 | 19 |
PageRank | References | Authors |
0.69 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| andreas weinmann | 1 | 138 | 12.81 |
| Laurent Demaret | 2 | 116 | 8.56 |
| Martin Storath | 3 | 138 | 12.69 |