Stable recovery of sparse signals with coherent tight frames via lp-analysis approach

DOI10.1142/S021953051650010XzbMath1366.94116OpenAlexW2346241328MaRDI QIDQ5269879

Song Li, Dekai Liu

Publication date: 28 June 2017

Published in: Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s021953051650010x

zbMATH Keywords

tight framessparsitycompressed sensing\(D\)-restricted isometry property\(l_p\)-minimization problem

Mathematics Subject Classification ID

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

Related Items (2)

Compressed data separation via unconstrained l1-split analysis ⋮ An analysis of noise folding for low-rank matrix recovery

Cites Work

• Recovery of sparsest signals via \(\ell^q \)-minimization
• Stable recovery of sparse signals via \(\ell_p\)-minimization
• Compressed sensing with coherent tight frames via \(l_q\)-minimization for \(0 < q \leq 1\)
• Compressed sensing with coherent and redundant dictionaries
• New bounds on the restricted isometry constant \(\delta _{2k}\)
• The restricted isometry property and its implications for compressed sensing
• Sharp RIP bound for sparse signal and low-rank matrix recovery
• Optimal \(D\)-RIP bounds in compressed sensing
• Sparse Recovery Algorithms: Sufficient Conditions in Terms of Restricted Isometry Constants
• Restricted isometry properties and nonconvex compressive sensing
• Decoding by Linear Programming
• Compressed Sensing and Redundant Dictionaries
• Iteratively reweighted least squares minimization for sparse recovery
• New Bounds for Restricted Isometry Constants With Coherent Tight Frames
• Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
• Compressed sensing

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