3 citations · 4 across the 12 of their papers we have counts for
15 papers
Gradient Descent on Point Clouds and Applications in Learned Operator Correction
Andreas Hauptmann, Yury Korolev, Matthew Thorpe
We consider the problem of minimising an energy over an unknown manifold that is given implicitly by a point cloud. For a known manifold one can define a gradient descent scheme an…
Enabling self-supervised learned primal dual with Noise2Inverse
Antti Sällinen, Siiri Rautio, Santeri Kaupinmäki +1
X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are noisy and incomplete. While lear…
QVaR: a Quantum Variational Regularization method for Linear Inverse Problems
Siiri Rautio, Hjørdis Schlüter, Andreas Hauptmann +1
We present a tailored framework for solving regularized linear inverse problems using quantum optimization methods. By discretizing the solution space and encoding data fidelity an…
Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts
Hanna Pulkkinen, Jenni Poimala, Leonid Kunyansky +2
We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited-view artifacts and noise comm…
Utilising a learned forward operator in the inverse problem of photoacoustic tomography
Karoliina Puronhaara, Teemu Sahlström, Andreas Hauptmann +1
We study the use of a learned forward operator in the inverse problem of photoacoustic tomography. The Fourier neural operator to approximate the photoacoustic wave propagation is…
Transformer Causality Regularization for Dynamic Inverse Problems
Gesa Sarnighausen, Anne Wald, Andreas Hauptmann
We study the concept of including the causality principle as regularizer into the solution of linear time-dependent inverse problems. This is achieved by combining transformer-base…
Learned iterative networks: An operator learning perspective
Andreas Hauptmann, Ozan Öktem
Learned image reconstruction has become a pillar in computational imaging and inverse problems. Among the most successful approaches are learned iterative networks, which are formu…
Towards robust quantitative photoacoustic tomography via learned iterative methods
Anssi Manninen, Janek Gröhl, Felix Lucka +1
Photoacoustic tomography (PAT) is a medical imaging modality that can provide high-resolution tissue images based on the optical absorption. Classical reconstruction methods for qu…
Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography
Alexander Denker, Fabio Margotti, Jianfeng Ning +5
Electrical Impedance Tomography (EIT) is a powerful imaging modality widely used in medical diagnostics, industrial monitoring, and environmental studies. The EIT inverse problem i…
Regularization for time-dependent inverse problems: Geometry of Lebesgue-Bochner spaces and algorithms
Gesa Sarnighausen, Thorsten Hohage, Martin Burger +2
We consider time-dependent inverse problems in a mathematical setting using Lebesgue-Bochner spaces. Such problems arise when one aims to recover a function from given observations…
Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography
J. Toivanen, V. Kolehmainen, A. Paldanius +3
Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to computationally expensive nonli…
Self-Supervised Denoiser Framework
Emilien Valat, Andreas Hauptmann, Ozan Öktem
Reconstructing images using Computed Tomography (CT) in an industrial context leads to specific challenges that differ from those encountered in other areas, such as clinical CT. I…
Model-based reconstructions for quantitative imaging in photoacoustic tomography
Andreas Hauptmann, Tanja Tarvainen
The reconstruction task in photoacoustic tomography can vary a lot depending on measured targets, geometry, and especially the quantity we want to recover. Specifically, as the sig…
Inverse Problems with Learned Forward Operators
Simon Arridge, Andreas Hauptmann, Yury Korolev
Solving inverse problems requires the knowledge of the forward operator, but accurate models can be computationally expensive and hence cheaper variants that do not compromise the…
Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches
Derick Nganyu Tanyu, Jianfeng Ning, Andreas Hauptmann +2
Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT…