Partial differential equations for zooming, deinterlacing and dejittering
In this paper, for imaging applications, we introduce partial differential equations (PDEs), which allow for correcting displacement errors, for dejittering, and for deinterlacing, respectively, in multi-channel data. These equations are derived via semi-groups for non-convex energy functionals. As...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2011
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| In: |
International journal of computer vision
Year: 2011, Jahrgang: 92, Heft: 2, Pages: 162-176 |
| ISSN: | 1573-1405 |
| DOI: | 10.1007/s11263-010-0326-x |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s11263-010-0326-x Verlag, lizenzpflichtig, Volltext: https://link.springer.com/article/10.1007/s11263-010-0326-x 
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| Verfasserangaben: | Frank Lenzen, Otmar Scherzer |
MARC
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| 520 | |a In this paper, for imaging applications, we introduce partial differential equations (PDEs), which allow for correcting displacement errors, for dejittering, and for deinterlacing, respectively, in multi-channel data. These equations are derived via semi-groups for non-convex energy functionals. As a particular example, for gray valued data, we find the mean curvature equation and the corresponding non-convex energy functional. As a further application for correction of displacement errors we study image interpolation, in particular zooming, of digital color images. For actual image zooming, the solutions of the proposed PDEs are projected onto a space of functions satisfying interpolation constraints. A comparison of the test results with standard and state-of-the-art interpolation algorithms shows the competitiveness of this approach. | ||
| 650 | 4 | |a Deinterlacing | |
| 650 | 4 | |a Dejittering | |
| 650 | 4 | |a Non-convex semigroups | |
| 650 | 4 | |a Partial differential equations | |
| 650 | 4 | |a Zooming | |
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