Name
Abstract | ||
|---|---|---|
In this paper we introduce the notion of second-order total generalized variation (TGV) regularization for manifold-valued data in a discrete setting. We provide an axiomatic approach to formalize reasonable generalizations of TGV to the manifold setting and present two possible concrete instances that fulfill the proposed axioms. We provide well-posedness results and present algorithms for a numerical realization of these generalizations to the manifold setup. Further, we provide experimental results for synthetic and real data to further underpin the proposed generalization numerically and show its potential for applications with manifold-valued data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1137/17M1147597 | SIAM JOURNAL ON IMAGING SCIENCES |
Keywords | Field | DocType |
total generalized variation,manifold-valued data,denoising,higher order regularization | Noise reduction,Applied mathematics,Mathematical optimization,Axiomatic system,Regularization (mathematics),Mathematics,Total generalized variation,Manifold | Journal |
Volume | Issue | ISSN |
11 | 3 | 1936-4954 |
Citations | PageRank | References |
4 | 0.39 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kristian Bredies | 1 | 497 | 24.54 |
| Martin Holler | 2 | 80 | 6.32 |
| Martin Storath | 3 | 138 | 12.69 |
| andreas weinmann | 4 | 138 | 12.81 |