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Markus Haltmeier

Showing results (1-10 of 32) with videos related to

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IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|April 13, 2016
Sampling Conditions for the Circular Radon TransformMarkus Haltmeier
Journal of Imaging|November 25, 2021
Discretization of Learned NETT Regularization for Solving Inverse ProblemsStephan Antholzer, Markus Haltmeier
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|February 13, 2024
Plug-and-Play Image Reconstruction Is a Convergent Regularization MethodAndrea Ebner, Markus Haltmeier
Applied Mathematics and Computation|February 21, 2012
Markus Grasmair, Markus Haltmeier, Otmar Scherzer
Inverse Problems in Science and Engineering|May 7, 2019
Deep learning for photoacoustic tomography from sparse dataStephan Antholzer, Markus Haltmeier, Johannes Schwab
IEEE Transactions on Medical Imaging|November 4, 2009
A reconstruction algorithm for photoacoustic imaging based on the nonuniform FFTMarkus Haltmeier, Otmar Scherzer, Gerhard Zangerl
Entropy (Basel, Switzerland)|December 3, 2020
Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic TomographySimon Rabanser, Lukas Neumann, Markus Haltmeier
Journal of Mathematical Imaging and Vision|April 21, 2020
Big in Japan: Regularizing Networks for Solving Inverse ProblemsJohannes Schwab, Stephan Antholzer, Markus Haltmeier
Journal of Imaging|May 24, 2024
Derivative-Free Iterative One-Step Reconstruction for Multispectral CTThomas Prohaszka, Lukas Neumann, Markus Haltmeier
Medical Physics|March 2, 2021
An end-to-end-trainable iterative network architecture for accelerated radial multi-coil 2D cine MR image reconstructionAndreas Kofler, Markus Haltmeier, Tobias Schaeffter, et al.
Pageof 4

Showing results (1-10 of 32) with videos related to

Sort By:
Pageof 4
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|April 13, 2016
Sampling Conditions for the Circular Radon TransformMarkus Haltmeier
Journal of Imaging|November 25, 2021
Discretization of Learned NETT Regularization for Solving Inverse ProblemsStephan Antholzer, Markus Haltmeier
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|February 13, 2024
Plug-and-Play Image Reconstruction Is a Convergent Regularization MethodAndrea Ebner, Markus Haltmeier
Applied Mathematics and Computation|February 21, 2012
Markus Grasmair, Markus Haltmeier, Otmar Scherzer
Inverse Problems in Science and Engineering|May 7, 2019
Deep learning for photoacoustic tomography from sparse dataStephan Antholzer, Markus Haltmeier, Johannes Schwab
IEEE Transactions on Medical Imaging|November 4, 2009
A reconstruction algorithm for photoacoustic imaging based on the nonuniform FFTMarkus Haltmeier, Otmar Scherzer, Gerhard Zangerl
Entropy (Basel, Switzerland)|December 3, 2020
Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic TomographySimon Rabanser, Lukas Neumann, Markus Haltmeier
Journal of Mathematical Imaging and Vision|April 21, 2020
Big in Japan: Regularizing Networks for Solving Inverse ProblemsJohannes Schwab, Stephan Antholzer, Markus Haltmeier
Journal of Imaging|May 24, 2024
Derivative-Free Iterative One-Step Reconstruction for Multispectral CTThomas Prohaszka, Lukas Neumann, Markus Haltmeier
Medical Physics|March 2, 2021
An end-to-end-trainable iterative network architecture for accelerated radial multi-coil 2D cine MR image reconstructionAndreas Kofler, Markus Haltmeier, Tobias Schaeffter, et al.
Pageof 4