# Regularization of Inverse Problems by Filtered Diagonal Frame Decomposition

@article{Ebner2020RegularizationOI, title={Regularization of Inverse Problems by Filtered Diagonal Frame Decomposition}, author={Andrea Ebner and Jurgen Frikel and Dirk A. Lorenz and Johannes Schwab and Markus Haltmeier}, journal={ArXiv}, year={2020}, volume={abs/2008.06219} }

## 4 Citations

### Translation invariant diagonal frame decomposition of inverse problems and their regularization

- MathematicsArXiv
- 2022

Solving inverse problems is central to a variety of important applications, such as biomedi-cal image reconstruction and non-destructive testing. These problems are characterized by the sensitivity…

### On regularization via frame decompositions with applications in tomography

- MathematicsInverse Problems
- 2022

This paper proves convergence for a general class of continuous regularization methods and derive convergence rates under both a-priori and a-posteriori parameter choice rules and applies the derived results to a standard tomography problem based on the Radon transform.

### Minimax detection of localized signals in statistical inverse problems

- MathematicsArXiv
- 2021

This work investigates minimax testing for detecting local signals or linear combinations of such signals when only indirect data is available and describes upper and lower bounds for the minimal size of the signal such that testing with small error probabilities is possible.

### $\ast$-Operator frame for $Hom_{\mathcal{A}}^{\ast}(\mathcal{X})$

- Mathematics
- 2021

. The concept of operator frame can be considered as a generalization of frame. Firstly, we introduce the notion of operator frame for the set of all adjointable operators Hom ∗A ( X ) on a Hilbert…

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This paper proves convergence for a general class of continuous regularization methods and derive convergence rates under both a-priori and a-posteriori parameter choice rules and applies the derived results to a standard tomography problem based on the Radon transform.

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