The following pages link to Felix Lucka (Q1785030):
Displaying 23 items.
- Risk estimators for choosing regularization parameters in ill-posed problems -- properties and limitations (Q1785032) (← links)
- Atomic super-resolution tomography (Q2037373) (← links)
- On the adjoint operator in photoacoustic tomography (Q2835445) (← links)
- Fast Gibbs sampling for high-dimensional Bayesian inversion (Q2835454) (← links)
- Maximum<i>a posteriori</i>estimates in linear inverse problems with log-concave priors are proper Bayes estimators (Q2936498) (← links)
- Erratum: Fast Gibbs sampling for high-dimensional Bayesian inversion (2016 <i>Inverse Problems</i> <b>32</b> 115019) (Q2971442) (← links)
- A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging (Q4571033) (← links)
- Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors (Q4920045) (← links)
- High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion (Q5019933) (← links)
- (Q5179269) (← links)
- Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation (Q5230411) (← links)
- Risk Estimators for Choosing Regularization Parameters in Ill-Posed Problems - Properties and Limitations (Q6282070) (← links)
- High Resolution 3D Ultrasonic Breast Imaging by Time-Domain Full Waveform Inversion (Q6359537) (← links)
- Existence of solutions to \(k\)-Wave models of nonlinear ultrasound propagation in biological tissue (Q6661980) (← links)
- Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8 (Q6698186) (← links)
- Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 9-16 (Q6698192) (← links)
- Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 17-24 (Q6698198) (← links)
- Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 25-32 (Q6698204) (← links)
- Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 33-37 (Q6698210) (← links)
- Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 38-42 (Q6698216) (← links)
- X-ray light-field - Gel bubbles - 1 deg angular range (Q6698219) (← links)
- Slice-by-Slice X-ray Tomography dataset of Dog Toy (Q6698231) (← links)
- X-ray Computed Tomography Case Study: Triangle and Pentagon Datasets with Various Sizes, Scales, and Noise Levels (Q6698253) (← links)
