11 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…
Learned iterative networks: An operator learning perspective
Andreas Hauptmann, Ozan Ãktem
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…
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…
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 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…