Compressed sensing with coherent tight frames via \(l_q\)-minimization for \(0 < q \leq 1\)

DOI10.3934/ipi.2014.8.761zbMath1366.94114arXiv1105.3299OpenAlexW2963519978MaRDI QIDQ479885

Song Li, Jun Hong Lin

Publication date: 5 December 2014

Published in: Inverse Problems and Imaging (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1105.3299

zbMATH Keywords

coherencetight framessparse recoverycompressed sensing\(D\)-restricted isometry property\(l_q\)-minimization

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) General harmonic expansions, frames (42C15)

Related Items (10)

Compressed data separation via unconstrained l1-split analysis ⋮ Improved RIP conditions for compressed sensing with coherent tight frames ⋮ Performance analysis for unconstrained analysis based approaches* ⋮ Recovery analysis for block ℓp − ℓ1 minimization with prior support information ⋮ On the null space property of \(l_q\)-minimization for \(0 < q \leq 1\) in compressed sensing ⋮ Sparse recovery with coherent tight frames via analysis Dantzig selector and analysis LASSO ⋮ Iterative re-weighted least squares algorithm for \(l_p\)-minimization with tight frame and \(0 < p \leq 1\) ⋮ Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization ⋮ Stable recovery of sparse signals with coherent tight frames via lp-analysis approach ⋮ Optimal \(D\)-RIP bounds in compressed sensing

Uses Software

• CoSaMP

Cites Work

• Recovery of sparsest signals via \(\ell^q \)-minimization
• Compressed sensing with coherent and redundant dictionaries
• New bounds on the restricted isometry constant \(\delta _{2k}\)
• A note on the complexity of \(L _{p }\) minimization
• The restricted isometry property and its implications for compressed sensing
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
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• A simple proof of the restricted isometry property for random matrices
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• Decoding by Linear Programming
• Greed is Good: Algorithmic Results for Sparse Approximation
• Compressed Sensing and Redundant Dictionaries
• Iteratively reweighted least squares minimization for sparse recovery
• Shifting Inequality and Recovery of Sparse Signals
• Sparse Approximate Solutions to Linear Systems
• Matching pursuits with time-frequency dictionaries
• Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$
• New Bounds for Restricted Isometry Constants
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing

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