Total Variation in Imaging

DOI10.1007/978-1-4939-0790-8_23zbMath1331.68264OpenAlexW2097335885MaRDI QIDQ2789827

Antonin Chambolle, Vincent Caselles, Matteo Novaga

Publication date: 2 March 2016

Published in: Handbook of Mathematical Methods in Imaging (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-1-4939-0790-8_23

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Variational methods for second-order elliptic equations (35J20)

Related Items (15)

A Maximum Principle Argument for the Uniform Convergence of Graph Laplacian Regressors ⋮ From graph cuts to isoperimetric inequalities: convergence rates of Cheeger cuts on data clouds ⋮ On the existence and stability of minimizers for generalized Tikhonov functionals with general similarity data ⋮ A non-convex denoising model for impulse and Gaussian noise mixture removing using bi-level parameter identification ⋮ A computational framework for edge-preserving regularization in dynamic inverse problems ⋮ On and Beyond Total Variation Regularization in Imaging: The Role of Space Variance ⋮ A Nonlocal Graph-PDE and Higher-Order Geometric Integration for Image Labeling ⋮ A New Operator Splitting Method for the Euler Elastica Model for Image Smoothing ⋮ Isotropic and anisotropic total variation regularization in electrical impedance tomography ⋮ Reconstruction Methods in THz Single-Pixel Imaging ⋮ On Decomposition Models in Imaging Sciences and Multi-time Hamilton--Jacobi Partial Differential Equations ⋮ Approximating the total variation with finite differences or finite elements ⋮ Unnamed Item ⋮ Convergence of Level Sets in Total Variation Denoising Through Variational Curvatures in Unbounded Domains ⋮ Generalized Intersection Algorithms with Fixed Points for Image Decomposition Learning

Uses Software

• MAXFLOW

Cites Work

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