Richard Huber
University of Graz
AlgorithmImage (mathematics)PhysicsInverse problemBiomedical engineeringElectronRegularization (physics)PixelTomographyBiological systemMaterials scienceRadon transformDynamic dataVariation (linguistics)Applied mathematicsSTEM TomographyJoint reconstructionMulti channelMultiple dataTotal generalized variationParallel beamSpectroscopyMathematicsComputer scienceSource codeNanoscopic scaleSpectral lineOptoelectronicsDark field microscopyComputed tomographyRadonImage qualityOperator normModalConvergence (routing)DiscretizationElectron tomographyRegularization (mathematics)Detector
9Publications
2H-index
29Citations
Publications 7
Newest
This paper presents a novel mathematical framework for understanding pixel-driven approaches for the parallel beam Radon transform as well as for the fanbeam transform, showing that with the correc...
1 CitationsSource
#1Georg HaberfehlnerH-Index: 13
#2Richard Huber (University of Graz)H-Index: 2
Last. Gerald KothleitnerH-Index: 10
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Source
#1Richard Huber (University of Graz)H-Index: 2
#2Georg HaberfehlnerH-Index: 13
Last. Kristian Bredies (University of Graz)H-Index: 31
view all 5 authors...
#1Richard Huber (University of Graz)H-Index: 2
#2Georg Haberfehlner (Graz University of Technology)H-Index: 13
Last. Kristian Bredies (University of Graz)H-Index: 31
view all 5 authors...
In multi-modal electron tomography, tilt series of several signals such as X-ray spectra, electron energy-loss spectra, annular dark-field, or bright-field data are acquired at the same time in a transmission electron microscope and subsequently reconstructed in three dimensions. However, the acquired data are often incomplete and suffer from noise, and generally each signal is reconstructed independently of all other signals, not taking advantage of correlation between different datasets. This ...
17 CitationsSource
#1Martin Holler (University of Graz)H-Index: 15
#2Richard Huber (University of Graz)H-Index: 2
Last. Florian Knoll (NYU: New York University)H-Index: 24
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We consider a class of regularization methods for inverse problems where a coupled regularization is employed for the simultaneous reconstruction of data from multiple sources. Applications for such a setting can be found in multi-spectral or multimodality inverse problems, but also in inverse problems with dynamic data. We consider this setting in a rather general framework and derive stability and convergence results, including convergence rates. In particular, we show how parameter choice str...
15 CitationsSource
#1Georg HaberfehlnerH-Index: 13
#2Richard Huber (University of Graz)H-Index: 2
Last. Kristian Bredies (University of Graz)H-Index: 31
view all 5 authors...
#1Richard Huber (University of Graz)H-Index: 2
#2Kristian Bredies (University of Graz)H-Index: 31
Last. Martin Holler (University of Graz)H-Index: 15
view all 4 authors...