22 citations · 84 across the 21 of their papers we have counts for
10 papers · 1 filter
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…
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…
Image reconstruction in light-sheet microscopy: spatially varying deconvolution and mixed noise
Bogdan Toader, Jerome Boulanger, Yury Korolev +4
We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial…
Data driven reconstruction using frames and Riesz bases
Andrea Aspri, Leon Frischauf, Yury Korolev +1
We study the problem of regularization of inverse problems adopting a purely data driven approach, by using the similarity to the method of regularization by projection. We provide…
Variational regularisation for inverse problems with imperfect forward operators and general noise models
Leon Bungert, Martin Burger, Yury Korolev +1
We study variational regularisation methods for inverse problems with imperfect forward operators whose errors can be modelled by order intervals in a partial order of a Banach lat…
Total Variation Regularisation with Spatially Variable Lipschitz Constraints
Martin Burger, Yury Korolev, Simone Parisotto +1
We introduce a first order Total Variation type regulariser that decomposes a function into a part with a given Lipschitz constant (which is also allowed to vary spatially) and a j…
Data driven regularization by projection
Andrea Aspri, Yury Korolev, Otmar Scherzer
We study linear inverse problems under the premise that the forward operator is not at hand but given indirectly through some input-output training pairs. We demonstrate that regul…
A total variation based regularizer promoting piecewise-Lipschitz reconstructions
Martin Burger, Yury Korolev, Carola-Bibiane Schönlieb +1
We introduce a new regularizer in the total variation family that promotes reconstructions with a given Lipschitz constant (which can also vary spatially). We prove regularizing pr…
Convergence rates and structure of solutions of inverse problems with imperfect forward models
Martin Burger, Yury Korolev, Julian Rasch
The goal of this paper is to further develop an approach to inverse problems with imperfect forward operators that is based on partially ordered spaces. Studying the dual problem y…
Image reconstruction with imperfect forward models and applications in deblurring
Yury Korolev, Jan Lellmann
We present and analyse an approach to image reconstruction problems with imperfect forward models based on partially ordered spaces - Banach lattices. In this approach, errors in t…