6 papers
The data-driven extreme value distribution: non-parametric tail estimation with a derived stability criterion
Michael Sandbichler, Tobias Hell
Quantifying the likelihood of extreme events underpins risk assessment, yet classical Extreme Value Theory relies on asymptotic assumptions that fail in the data-sparse, non-statio…
Data driven extreme value distribution estimation: Derivation of the Mean Integrated Squared Error, optimal bandwidth selection and stability conditions
Michael Sandbichler, Tobias Hell
We introduce the data driven extreme value distribution (DDEVD) estimator, a kernel-based method for estimating extreme value distributions from data. We derive its mean integrated…
A New Sparsification and Reconstruction Strategy for Compressed Sensing Photoacoustic Tomography
Markus Haltmeier, Michael Sandbichler, Thomas Berer +3
Compressed sensing (CS) is a promising approach to reduce the number of measurements in photoacoustic tomography (PAT) while preserving high spatial resolution. This allows to incr…
Compressive Time-of-Flight 3D Imaging Using Block-Structured Sensing Matrices
Stephan Antholzer, Christoph Wolf, Michael Sandbichler +2
Spatially and temporally highly resolved depth information enables numerous applications including human-machine interaction in gaming or safety functions in the automotive industr…
Total Variation Minimization in Compressed Sensing
Felix Krahmer, Christian Kruschel, Michael Sandbichler
This chapter gives an overview over recovery guarantees for total variation minimization in compressed sensing for different measurement scenarios. In addition to summarizing the r…
Online and Stable Learning of Analysis Operators
Michael Sandbichler, Karin Schnass
In this paper four iterative algorithms for learning analysis operators are presented. They are built upon the same optimisation principle underlying both Analysis K-SVD and Analys…