On sparsity maximization in tomographic particle image reconstruction
This work focuses on tomographic image reconstruction in experimental fluid mechanics (TomoPIV), a recently established 3D particle image velocimetry technique. Corresponding 2D image sequences (projections) and the 3D reconstruction via tomographical methods provides the basis for estimating turbul...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Kapitel/Artikel Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
2008
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| In: |
Pattern recognition
Year: 2008, Pages: 294-303 |
| DOI: | 10.1007/978-3-540-69321-5_30 |
| Online-Zugang: | Resolving-System, Volltext: http://dx.doi.org/10.1007/978-3-540-69321-5_30 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-540-69321-5_30 
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| Verfasserangaben: | Stefania Petra, Andreas Schröder, Bernhard Wieneke, Christoph Schnörr |
| Zusammenfassung: | This work focuses on tomographic image reconstruction in experimental fluid mechanics (TomoPIV), a recently established 3D particle image velocimetry technique. Corresponding 2D image sequences (projections) and the 3D reconstruction via tomographical methods provides the basis for estimating turbulent flows and related flow patterns through image processing. TomoPIV employs undersampling to make the high-speed imaging process feasible, resulting in an ill-posed image reconstruction problem. We address the corresponding basic problems involved and point out promising optimization criteria for reconstruction based on sparsity maximization, that perform favorably in comparison to classical algebraic methods currently in use for TomoPIV. |
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| Beschreibung: | Gesehen am 26.07.2018 |
| Beschreibung: | Online Resource |
| ISBN: | 9783540693215 |
| DOI: | 10.1007/978-3-540-69321-5_30 |