Greedy algorithms for image approximation from scattered Radon data

Publikationstyp
Conference Paper
Publikationsdatum
2021-12-14
Sprache
English
Author
Albrecht, Kristof 
Iske, Armin 
Institut
Mathematik E-10 
DOI
10.15480/882.4771
TORE-URI
http://hdl.handle.net/11420/14319
Enthalten in
Proceedings in applied mathematics and mechanics 
Volume
21
Issue
1
Article Number
e202100223
Citation
Proceedings in applied mathematics and mechanics 21 (1): e202100223 (2021-12-14)
Contribution to Conference
92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2022 
Publisher DOI
10.1002/pamm.202100223
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
Projekt(e)
Projekt DEAL 
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG) 
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”.
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by-nc-nd/4.0/
Thumbnail Image
Name

Proc Appl Math Mech - 2021 - Albrecht - Greedy algorithms for image approximation from scattered Radon data.pdf

Size

342.92 KB

Format

Adobe PDF