Total variation time flow with quantile regression for image restoration

DOI10.1016/j.jmaa.2013.02.029zbMath1282.49002OpenAlexW2057185736WikidataQ113100971 ScholiaQ113100971MaRDI QIDQ394951

Thomas Wunderli

Publication date: 28 January 2014

Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmaa.2013.02.029

zbMATH Keywords

quantile regressionimage restorationspace of bounded variationstime flows

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Sensitivity, stability, well-posedness (49K40) Computing methodologies for image processing (68U10) Existence theories for free problems in two or more independent variables (49J10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

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• Nonlinear total variation based noise removal algorithms
• The total variation flow in \(\mathbb R^N\)
• \(G\)-norm properties of bounded variation regularization
• On time flows of minimizers of general convex functionals of linear growth with variable exponent in BV space and stability of pseudosolutions
• Elastic plastic deformation
• Convex functionals and partial regularity
• An evolution problem for plastic antiplanar shear
• Asymptotic convergence of nonlinear contraction semigroups in Hilbert space
• Image recovery via total variation minimization and related problems
• Parabolic quasilinear equations minimizing linear growth functionals
• Some qualitative properties for the total variation flow
• Minimizing total variation flow
• Adaptive total variation for image restoration in BV space
• Lower semicontinuity problems in Sobolev spaces with respect to a measure
• Extensions of smoothing via taut strings
• Total Variation Regularization for Image Denoising, II. Examples
• Total Variation Regularization for Image Denoising, III. Examples.
• Total Variation Regularization for Image Denoising, I. Geometric Theory
• Digital filters as absolute norm regularizers
• Regression Quantiles
• Decomposition of images by the anisotropic Rudin-Osher-Fatemi model
• Recovery of Blocky Images from Noisy and Blurred Data
• THE MINIMIZING TOTAL VARIATION FLOW WITH MEASURE INITIAL CONDITIONS
• Aspects of Total Variation RegularizedL¹Function Approximation
• Variable Exponent, Linear Growth Functionals in Image Restoration
• A study in the BV space of a denoising-deblurring variational problem

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