Estimation of q for $\ell _{q}$-minimization in signal recovery with tight frame

DOI10.1186/S13660-023-03068-ZMaRDI QIDQ6498644

Wenfeng Zhang, Kaihao Liang, Chaolong Zhang

Publication date: 7 May 2024

Published in: Journal of Inequalities and Applications (Search for Journal in Brave)

Mathematics Subject Classification ID

Communication, information (94Axx) Approximations and expansions (41Axx) Nontrigonometric harmonic analysis (42Cxx)

Cites Work

• Compressed sensing with coherent tight frames via \(l_q\)-minimization for \(0 < q \leq 1\)
• The null space property for sparse recovery from multiple measurement vectors
• Compressed sensing with coherent and redundant dictionaries
• Highly sparse representations from dictionaries are unique and independent of the sparseness measure
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• Sharp RIP bound for sparse signal and low-rank matrix recovery
• 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
• Optimal \(D\)-RIP bounds in compressed sensing
• Frames and bases. An introductory course
• On sparse reconstruction from Fourier and Gaussian measurements
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Compressed Sensing and Redundant Dictionaries
• Atomic Decomposition by Basis Pursuit
• Iteratively reweighted algorithm for signals recovery with coherent tight frame
• Sparse Approximate Solutions to Linear Systems
• Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ ¹ minimization
• Stable signal recovery from incomplete and inaccurate measurements
• Provable Compressed Sensing With Generative Priors via Langevin Dynamics

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