no code implementations • 21 Nov 2023 • Simon Arridge, Andreas Hauptmann, Yury Korolev
The first one is completely agnostic to the forward operator and learns its restriction to the subspace spanned by the training data.
1 code implementation • 9 Jun 2022 • Tamara G. Grossmann, Sören Dittmer, Yury Korolev, Carola-Bibiane Schönlieb
Inspired by and extending the framework of physics-informed neural networks (PINNs), we propose the TVflowNET, an unsupervised neural network approach, to approximate the solution of the TV flow given an initial image and a time instance.
no code implementations • 8 Aug 2021 • Bogdan Toader, Jerome Boulanger, Yury Korolev, Martin O. Lenz, James Manton, Carola-Bibiane Schonlieb, Leila Muresan
Then, we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. "Infimal convolution of data discrepancies for mixed noise removal", SIAM Journal on Imaging Sciences 10. 3 (2017), 1196-1233.
no code implementations • 5 May 2021 • Yury Korolev
We study two-layer neural networks whose domain and range are Banach spaces with separable preduals.
1 code implementation • NeurIPS 2020 • Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola-Bibiane Schönlieb
To the best of our knowledge, this is the first approach towards learning a non-linear spectral decomposition of images.
no code implementations • 28 May 2020 • Leon Bungert, Martin Burger, Yury Korolev, Carola-Bibiane Schoenlieb
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 lattice.
Numerical Analysis Numerical Analysis Optimization and Control 47A52, 65J20, 65J22, 65K10
1 code implementation • 5 Dec 2019 • Martin Burger, Yury Korolev, Simone Parisotto, Carola-Bibiane Schönlieb
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 jump part.
Numerical Analysis Numerical Analysis 65J20, 65J22, 68U10, 94A08
no code implementations • 12 Mar 2019 • Martin Burger, Yury Korolev, Carola-Bibiane Schönlieb, Christiane Stollenwerk
We introduce a new regularizer in the total variation family that promotes reconstructions with a given Lipschitz constant (which can also vary spatially).
no code implementations • 3 Aug 2017 • Yury Korolev, Jan Lellmann
In this approach, errors in the data and in the forward models are described using order intervals.
no code implementations • 7 Sep 2015 • Artur Gorokh, Yury Korolev, Tuomo Valkonen
Errors in the data and the forward operator of an inverse problem can be handily modelled using partial order in Banach lattices.