Name
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 l(p)-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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1137/120882706 | SIAM JOURNAL ON IMAGING SCIENCES |
Keywords | Field | DocType |
regularization parameter,image denoising,learning theory,nondifferentiable optimization,bilevel optimization,semismooth Newton algorithm | Mathematical optimization,Basis pursuit denoising,Bilevel optimization,Mathematical analysis,Learning theory,Parameter learning,Ground truth,Operator (computer programming),Image denoising,Prior probability,Mathematics | Journal |
Volume | Issue | ISSN |
6 | 2 | 1936-4954 |
Citations | PageRank | References |
33 | 1.19 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karl Kunisch | 1 | 1370 | 145.58 |
| Thomas Pock | 2 | 3858 | 174.49 |