Block-sparse compressed sensing: non-convex model and iterative re-weighted algorithm

DOI10.1080/17415977.2012.677444zbMath1326.94031OpenAlexW2124698503MaRDI QIDQ5300386

Leyuan Fang, Hai-Tao Yin, Shutao Li

Publication date: 27 June 2013

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

Full work available at URL: https://doi.org/10.1080/17415977.2012.677444

zbMATH Keywords

block-sparse recoverynon-convex modelblock-sparse compressed sensingiterative re-weighted algorithm

Mathematics Subject Classification ID

Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

• Enhancing sparsity by reweighted \(\ell _{1}\) minimization
• Atomic Decomposition by Basis Pursuit
• Restricted isometry properties and nonconvex compressive sensing
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Decoding by Linear Programming
• Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
• Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals
• Matching pursuits with time-frequency dictionaries
• Iterative thresholding compressed sensing MRI based on contourlet transform
• Robust Recovery of Signals From a Structured Union of Subspaces
• Model-Based Compressive Sensing
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing

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