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Author Kunisch, Karl ♦ Pock, Thomas
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Variational Model ♦ Parameter Learning ♦ Bilevel Optimization Approach ♦ Lower-level Problem ♦ Bilevel Optimization Problem ♦ Analysis Prior ♦ Linear Operator ♦ Loss Function ♦ State-of-the-art Performance ♦ Semismooth Newton Method ♦ Higher-level Problem ♦ Fixed Set ♦ Variational Image ♦ Optimized Image ♦ Learning Problem ♦ Ground Truth Data
Abstract In this work we consider the problem of parameter learning for variational image denoising models. The learning problem is formulated as a bilevel optimization problem, where the lower-level problem is given by the variational model and the higher-level problem is expressed by means of 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 ℓp-norm–based analysis priors using a fixed set of linear operators. We devise semismooth Newton methods for solving the resulting nonsmooth bilevel optimization problems and show that the optimized image denoising models can achieve state-of-the-art performance.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Publisher Date 2013-01-01