期刊
SIAM JOURNAL ON IMAGING SCIENCES
卷 6, 期 2, 页码 938-983出版社
SIAM PUBLICATIONS
DOI: 10.1137/120882706
关键词
regularization parameter; image denoising; learning theory; nondifferentiable optimization; bilevel optimization; semismooth Newton algorithm
类别
资金
- Austrian Science Fund (FWF) [F 3203] Funding Source: researchfish
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.
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