Compression and denoising using \(l _{0}\)-norm

DOI10.1007/s10589-010-9352-4zbMath1401.94055OpenAlexW1983679613MaRDI QIDQ763401

Andy C. Yau, Michael Kwok-Po Ng, Xue-Cheng Tai

Publication date: 9 March 2012

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-010-9352-4

zbMATH Keywords

\(l _{0}\)-norm data fittinggraph-cuts algorithmtotal variation minimization problem

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Application of orthogonal and other special functions (94A11)

Related Items (1)

A convex relaxation framework consisting of a primal-dual alternative algorithm for solving \(\ell_0\) sparsity-induced optimization problems with application to signal recovery based image restoration

Uses Software

• PDCO
• RecPF

Cites Work

• Nonlinear total variation based noise removal algorithms
• Some remarks on greedy algorithms
• Image restoration with discrete constrained total variation. I: Fast and exact optimization
• A Fast Total Variation Minimization Method for Image Restoration
• A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
• Image decomposition via the combination of sparse representations and a variational approach
• Fast Sparse Representation Based on Smoothed ℓ0 Norm
• Graph Cut Optimization for the Piecewise Constant Level Set Method Applied to Multiphase Image Segmentation
• A new approach to the maximum-flow problem
• Atomic Decomposition by Basis Pursuit
• Embedded image coding using zerotrees of wavelet coefficients
• Sparse Bayesian Learning for Basis Selection

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