Monday, 15:45 - 16:10 h, Room: H 1012

 

Thomas Pock
On parameter learning in variational models

Coauthor: Karl Kunisch

 

Abstract:
In this work we consider the problem of parameter learning for variational image denoising models. We formulate the learning problem as a bilevel optimization problem, where the lower level problem is given by the variational model and the higher level problem is given by a loss function that penalizes errors between the solution of the lower level problem and the ground truth data. We consider a class of image denoising models incorporating a sum of analysis based priors over a fixed set of linear operators. We devise semi-smooth Newton methods to solve the resulting non-smooth bilevel optimization problems and show that the optimized image denoising models can achieve state-of-the-art performance.

 

Talk 2 of the invited session Mon.3.H 1012
"Nonsmooth optimization in imaging sciences I" [...]
Cluster 17
"Nonsmooth optimization" [...]

 

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