Bilevel Image Denoising Using Gaussianity Tests

DOI10.1007/978-3-319-18461-6_10zbMath1444.94011OpenAlexW756745906MaRDI QIDQ3300293

Pierre Weiss, Jérôme Fehrenbach, Mila Nikolova, Gabriele Drauschke

Publication date: 28 July 2020

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-18461-6_10

zbMATH Keywords

convex optimizationbilevel programmingimage denoisingGaussianity tests

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (4)

Preface for Inverse Problems special issue on learning and inverse problems ⋮ The structure of optimal parameters for image restoration problems ⋮ Optimal selection of the regularization function in a weighted total variation model. I: Modelling and theory ⋮ Bilevel Methods for Image Reconstruction

Cites Work

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• Foundations of bilevel programming
• Fully smoothed \(\ell_1\)-\(TV\) models: bounds for the minimizers and parameter choice
• Model distortions in Bayesian MAP reconstruction
• A Bilevel Optimization Approach for Parameter Learning in Variational Models
• Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Fast Discrete Curvelet Transforms

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