An approach to vectorial total variation based on geometric measure theory
| dc.contributor.author | Goldlücke, Bastian | |
| dc.contributor.author | Cremers, Daniel | |
| dc.date.accessioned | 2017-11-29T14:42:59Z | |
| dc.date.available | 2017-11-29T14:42:59Z | |
| dc.date.issued | 2010-06 | eng |
| dc.description.abstract | We analyze a previously unexplored generalization of the scalar total variation to vector-valued functions, which is motivated by geometric measure theory. A complete mathematical characterization is given, which proves important invariance properties as well as existence of solutions of the vectorial ROF model. As an important feature, there exists a dual formulation for the proposed vectorial total variation, which leads to a fast and stable minimization algorithm. The main difference to previous approaches with similar properties is that we penalize across a common edge direction for all channels, which is a major theoretical advantage. Experiments show that this leads to a significiantly better restoration of color edges in practice. | eng |
| dc.description.version | published | eng |
| dc.identifier.doi | 10.1109/CVPR.2010.5540194 | eng |
| dc.identifier.uri | https://kops.uni-konstanz.de/handle/123456789/40773 | |
| dc.language.iso | eng | eng |
| dc.subject.ddc | 004 | eng |
| dc.title | An approach to vectorial total variation based on geometric measure theory | eng |
| dc.type | INPROCEEDINGS | eng |
| dspace.entity.type | Publication | |
| kops.citation.bibtex | @inproceedings{Goldlucke2010-06appro-40773,
year={2010},
doi={10.1109/CVPR.2010.5540194},
title={An approach to vectorial total variation based on geometric measure theory},
isbn={978-1-4244-6984-0},
issn={1063-6919},
publisher={IEEE},
address={Piscataway, NJ},
booktitle={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, California, USA, 13-18 June 2010; Vol. 1},
pages={327--333},
author={Goldlücke, Bastian and Cremers, Daniel}
} | |
| kops.citation.iso690 | GOLDLÜCKE, Bastian, Daniel CREMERS, 2010. An approach to vectorial total variation based on geometric measure theory. CVPR 2010. San Francisco, USA, 13. Juni 2010 - 18. Juni 2010. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, California, USA, 13-18 June 2010; Vol. 1. Piscataway, NJ: IEEE, 2010, pp. 327-333. ISSN 1063-6919. ISBN 978-1-4244-6984-0. Available under: doi: 10.1109/CVPR.2010.5540194 | deu |
| kops.citation.iso690 | GOLDLÜCKE, Bastian, Daniel CREMERS, 2010. An approach to vectorial total variation based on geometric measure theory. CVPR 2010. San Francisco, USA, Jun 13, 2010 - Jun 18, 2010. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, California, USA, 13-18 June 2010; Vol. 1. Piscataway, NJ: IEEE, 2010, pp. 327-333. ISSN 1063-6919. ISBN 978-1-4244-6984-0. Available under: doi: 10.1109/CVPR.2010.5540194 | eng |
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| kops.conferencefield | CVPR 2010, 13. Juni 2010 - 18. Juni 2010, San Francisco, USA | deu |
| kops.date.conferenceEnd | 2010-06-18 | eng |
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| kops.sourcefield.plain | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, California, USA, 13-18 June 2010; Vol. 1. Piscataway, NJ: IEEE, 2010, pp. 327-333. ISSN 1063-6919. ISBN 978-1-4244-6984-0. Available under: doi: 10.1109/CVPR.2010.5540194 | deu |
| kops.sourcefield.plain | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, California, USA, 13-18 June 2010; Vol. 1. Piscataway, NJ: IEEE, 2010, pp. 327-333. ISSN 1063-6919. ISBN 978-1-4244-6984-0. Available under: doi: 10.1109/CVPR.2010.5540194 | eng |
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| source.title | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, California, USA, 13-18 June 2010; Vol. 1 | eng |