Nonlinear multilayered representation of graph-signals

DOI10.1007/s10851-012-0348-9zbMath1276.94006OpenAlexW2095499898MaRDI QIDQ385311

Olivier Lézoray, Moncef Hidane, Abderrahim Elmoataz

Publication date: 2 December 2013

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10851-012-0348-9

zbMATH Keywords

weighted graphshierarchical modelsmultiscale representationsnonlocal total variationsignal decomposition

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items

Variational models for signal processing with graph neural networks, \((\mathrm{BV},L^p)\)-decomposition, \(p = 1,2\), of functions in metric random walk spaces, Image segmentation with arbitrary noise models by solving minimal surface problems, Convex variational methods on graphs for multiclass segmentation of high-dimensional data and point clouds, A Graph Framework for Manifold-Valued Data, Hierarchical Construction of Bounded Solutions in Critical Regularity Spaces

Uses Software

• UNLocBoX

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nonlinear total variation based noise removal algorithms
• Linearized proximal alternating minimization algorithm for motion deblurring by nonlocal regularization
• Accelerated projected gradient method for linear inverse problems with sparsity constraints
• Convex analysis and nonlinear optimization. Theory and examples.
• Multiscale hierarchical decomposition of images with applications to deblurring, denoising, and segmentation
• Axioms and fundamental equations of image processing
• Image recovery via total variation minimization and related problems
• Modeling textures with total variation minimization and oscillating patterns in image processing
• An algorithm for total variation minimization and applications
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Non-local regularization of inverse problems
• Image decomposition into a bounded variation component and an oscillating component
• Dual norms and image decomposition models
• Proximal Splitting Methods in Signal Processing
• Nonlocal Operators with Applications to Image Processing
• A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
• The digital TV filter and nonlinear denoising
• Image Decomposition and Restoration Using Total Variation Minimization and theH¹
• Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage
• A Multiscale Image Representation Using Hierarchical (BV,L²) Decompositions
• Numerical Methods and Applications in Total Variation Image Restoration
• Image Denoising Methods. A New Nonlocal Principle
• Total Variation Projection With First Order Schemes
• Signal Recovery by Proximal Forward-Backward Splitting
• Scale Recognition, Regularization Parameter Selection, and Meyer's G Norm in Total Variation Regularization
• Proximité et dualité dans un espace hilbertien
• An Iterative Regularization Method for Total Variation-Based Image Restoration