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Greedy algorithms for image approximation from scattered Radon data
Citation Link: https://doi.org/10.15480/882.4771
Publikationstyp
Conference Paper
Date Issued
2021-12-14
Sprache
English
Author(s)
Albrecht, Kristof
Institut
TORE-DOI
Volume
21
Issue
1
Article Number
e202100223
Citation
Proceedings in applied mathematics and mechanics 21 (1): e202100223 (2021-12-14)
Contribution to Conference
Publisher DOI
Publisher
Wiley-VCH
Positive definite kernels are powerful tools for multivariate approximation from scattered data. This contribution discusses kernel-based image approximation from scattered Radon data. To this end, we use weighted kernels for the reconstruction. Moreover, we propose greedy algorithms, which are used to adaptively select suitable approximation spaces. This reduces the complexity of the resulting image reconstruction method and, moreover, it improves the numerical stability quite significantly.
DDC Class
600: Technik
620: Ingenieurwissenschaften
Funding(s)
Funding Organisations
More Funding Information
The authors acknowledge the support by the Deutsche Forschungsgemeinschaft (DFG) within the Research Training Group GRK 2583 “Modeling, Simulation and Optimization of Fluid Dynamic Applications”.
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Proc Appl Math Mech - 2021 - Albrecht - Greedy algorithms for image approximation from scattered Radon data.pdf
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