Andreas Hauptmann

Orcid: 0000-0002-3756-8121

According to our database1, Andreas Hauptmann authored at least 48 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2023
Sequential Model Correction for Nonlinear Inverse Problems.
SIAM J. Imaging Sci., December, 2023

Enhancement of instrumented ultrasonic tracking images using deep learning.
Int. J. Comput. Assist. Radiol. Surg., February, 2023

Learned Reconstruction Methods With Convergence Guarantees: A survey of concepts and applications.
IEEE Signal Process. Mag., 2023

Model-based reconstructions for quantitative imaging in photoacoustic tomography.
CoRR, 2023

Inverse Problems with Learned Forward Operators.
CoRR, 2023

Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches.
CoRR, 2023

Convergent regularization in inverse problems and linear plug-and-play denoisers.
CoRR, 2023

Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging.
CoRR, 2023

Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography.
CoRR, 2023

Robust Data-Driven Accelerated Mirror Descent.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
Guest Editorial: MLSP 2020 Special Issue.
J. Signal Process. Syst., 2022

A Model-Based Iterative Learning Approach for Diffuse Optical Tomography.
IEEE Trans. Medical Imaging, 2022

An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction.
IEEE Trans. Computational Imaging, 2022

Unsupervised denoising for sparse multi-spectral computed tomography.
CoRR, 2022

Sparsity promoting reconstructions via hierarchical prior models in diffuse optical tomography.
CoRR, 2022

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs.
CoRR, 2022

Learned reconstruction with convergence guarantees.
CoRR, 2022

2021
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems.
IEEE Trans. Computational Imaging, 2021

An Efficient Quasi-Newton Method for Nonlinear Inverse Problems via Learned Singular Values.
IEEE Signal Process. Lett., 2021

On Learned Operator Correction in Inverse Problems.
SIAM J. Imaging Sci., 2021

Learning and correcting non-Gaussian model errors.
J. Comput. Phys., 2021

Is Deep Image Prior in Need of a Good Education?
CoRR, 2021

Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data.
CoRR, 2021

Unsupervised Knowledge-Transfer for Learned Image Reconstruction.
CoRR, 2021

Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach.
IEEE Access, 2021

2020
Multi-Scale Learned Iterative Reconstruction.
IEEE Trans. Computational Imaging, 2020

Networks for Nonlinear Diffusion Problems in Imaging.
J. Math. Imaging Vis., 2020

Machine Learning in Magnetic Resonance Imaging: Image Reconstruction.
CoRR, 2020

Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction.
CoRR, 2020

Fusing electrical and elasticity imaging.
CoRR, 2020

Deep Learning in Photoacoustic Tomography: Current approaches and future directions.
CoRR, 2020

On the unreasonable effectiveness of CNNs.
CoRR, 2020

Image reconstruction in dynamic inverse problems with temporal models.
CoRR, 2020

Sequentially optimized projections in X-ray imaging.
CoRR, 2020

Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems.
CoRR, 2020

On Learned Operator Correction.
CoRR, 2020

Estimation of dynamic SNP-heritability with Bayesian Gaussian process models.
Bioinform., 2020

Blind Hierarchical Deconvolution.
Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

Material Decomposition Problem in Spectral CT: A Transfer Deep Learning Approach.
Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), 2020

Convolutional Neural Network for Material Decomposition in Spectral CT Scans.
Proceedings of the 28th European Signal Processing Conference, 2020

2019
Rapid Whole-Heart CMR with Single Volume Super-resolution.
CoRR, 2019

2018
Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography.
IEEE Trans. Medical Imaging, 2018

Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks.
IEEE Trans. Medical Imaging, 2018

Beltrami-Net: Domain Independent Deep D-bar Learning for Absolute Imaging with Electrical Impedance Tomography (a-EIT).
CoRR, 2018

Revealing cracks inside conductive bodies by electric surface measurements.
CoRR, 2018

Real-time Cardiovascular MR with Spatio-temporal De-aliasing using Deep Learning - Proof of Concept in Congenital Heart Disease.
CoRR, 2018

Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction.
Proceedings of the Machine Learning for Medical Image Reconstruction, 2018

2017
Model based learning for accelerated, limited-view 3D photoacoustic tomography.
CoRR, 2017


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