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Variational regularisation for inverse problems with imperfect forward operators and general noise models.

Published version
Peer-reviewed

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Authors

Schönlieb, Carola-Bibiane 

Abstract

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. We carry out analysis with respect to existence and convex duality for general data fidelity terms and regularisation functionals. Both for a priori and a posteriori parameter choice rules, we obtain convergence rates of the regularised solutions in terms of Bregman distances. Our results apply to fidelity terms such as Wasserstein distances, φ-divergences, norms, as well as sums and infimal convolutions of those.

Description

Funder: Cantab Capital Institute for the Mathematics of Information


Funder: National Physical Laboratory; doi: https://doi.org/10.13039/501100007851


Funder: Alan Turing Institute; doi: https://doi.org/10.13039/100012338

Keywords

Banach lattices, Bregman distances, Kullback–Leibler divergence, Wasserstein distances, discrepancy principle, f-divergences, imperfect forward models

Journal Title

Inverse Probl

Conference Name

Journal ISSN

0266-5611
1361-6420

Volume Title

36

Publisher

IOP Publishing
Sponsorship
Engineering and Physical Sciences Research Council (EP/M00483X/1)
Engineering and Physical Sciences Research Council (EP/N014588/1)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (777826)
Leverhulme Trust (PLP-2017-275)
National Physical Laboratory (NPL) (Unknown)
Alan Turing Institute (Unknown)
EPSRC (EP/S026045/1)
EPSRC (EP/T003553/1)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
EPSRC (EP/V003615/1)