Multiscale Texture Orientation Analysis Using Spectral Total-Variation Decomposition

DOI10.1007/978-3-319-18461-6_39zbMath1453.94009OpenAlexW2404125997MaRDI QIDQ3300331

Dikla Horesh, Guy Gilboa

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_39

zbMATH Keywords

total variationimage enhancementimage decompositionspectral total variationnonlinear eigenfunction analysis

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Nonlinear eigenvalue problems and nonlinear spectral theory for PDEs (35P30) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Quasilinear parabolic equations (35K59)

Related Items (2)

Nonlinear spectral analysis via one-homogeneous functionals: overview and future prospects ⋮ Inverse scale space decomposition

Cites Work

• The total variation flow in \(\mathbb R^N\)
• Evaluation of the effects of Gabor filter parameters on texture classification
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• Multi-class transductive learning based on \(\ell^1\) relaxations of Cheeger cut and Mumford-Shah-Potts model
• Structure-texture image decomposition -- modeling, algorithms, and parameter selection
• Ground states and singular vectors of convex variational regularization methods
• Spectral Decompositions Using One-Homogeneous Functionals
• Image Decomposition and Restoration Using Total Variation Minimization and theH¹
• A Multiscale Image Representation Using Hierarchical (BV,L²) Decompositions
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability
• Multiscale Texture Separation
• A Total Variation Spectral Framework for Scale and Texture Analysis
• A Spectral Approach to Total Variation

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