Digitalisierung
Refine
Year of publication
Document Type
- Part of a Book (36) (remove)
Is part of the Bibliography
- no (36) (remove)
Keywords
- Digitalisierung (4)
- Pflege (3)
- Schlaganfall (3)
- Datenschutz (2)
- Datensicherung (2)
- Logopädie (2)
- Roboter (2)
- Telenursing (2)
- 3D scanning (1)
- Adaptable Code (1)
Institute
- Fakultät Informatik und Mathematik (19)
- Labor für Technikfolgenabschätzung und Angewandte Ethik (LaTe) (10)
- Labor Parallele und Verteilte Systeme (7)
- Fakultät Angewandte Sozial- und Gesundheitswissenschaften (6)
- Institut für Sozialforschung und Technikfolgenabschätzung (IST) (5)
- Labor Pflegeforschung (LPF) (3)
- Regensburg Center of Health Sciences and Technology - RCHST (3)
- Fakultät Maschinenbau (2)
- Labor Logopädie (LP) (2)
- Labor eHealth (eH) (2)
Begutachtungsstatus
- peer-reviewed (2)
We analyze sparse frame based regularization of inverse problems by means of a diagonal frame decomposition (DFD) for the forward operator, which generalizes the SVD. The DFD allows to define a non-iterative (direct) operator-adapted frame thresholding approach which we show to provide a convergent regularization method with linear convergence rates. These results will be compared to the well-known analysis and synthesis variants of sparse ℓ1-regularization which are usually implemented thorough iterative schemes. If the frame is a basis (non-redundant case), the three versions of sparse regularization, namely synthesis and analysis variants of ℓ1-regularization as well as the DFD thresholding are equivalent. However, in the redundant case, those three approaches are pairwise different.