Learning optimal spatially-dependent regularization parameters in total variation image denoising
Cao Van Chung, Juan Carlos De los Reyes, Carola‐Bibiane Schönlieb · Inverse Problems · 2017
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 H 1 ( 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.