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Learning optimal spatially-dependent regularization parameters in total variation image denoising

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Van Chung, C 
De los Reyes, JC 
Schönlieb, CB 

Abstract

We consider a bilevel optimization approach in function space for the choice of spatially dependent regularization parameters in TV image denoising models. First- and second-order optimality conditions for the bilevel problem are studied when the spatially-dependent parameter belongs to the Sobolev space H1(Ω). A combined Schwarz domain decomposition-semismooth Newton method is proposed for the solution of the full optimality system and local superlinear convergence of the semismooth Newton method is verified. Exhaustive numerical computations are finally carried out to show the suitability of the approach.

Description

Keywords

optimization-based learning in imaging, bilevel optimization, PDE-constrained optimization, semismooth Newton method, Schwarz domain decomposition method

Journal Title

Inverse Problems

Conference Name

Journal ISSN

0266-5611
1361-6420

Volume Title

33

Publisher

IOP Science
Sponsorship
Engineering and Physical Sciences Research Council (EP/N014588/1)
Engineering and Physical Sciences Research Council (EP/M00483X/1)
Engineering and Physical Sciences Research Council (EP/J009539/1)
Alan Turing Institute (unknown)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)