Sparse signal reconstruction based on multiparameter approximation function with smoothed \(\ell_0\) norm
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Publication:1718317
DOI10.1155/2014/416542zbMath1407.94036OpenAlexW1979246694WikidataQ59066934 ScholiaQ59066934MaRDI QIDQ1718317
Ying-Qi Li, Xiao-Feng Fang, Jiang-She Zhang
Publication date: 8 February 2019
Published in: Mathematical Problems in Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2014/416542
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- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- Sharp RIP bound for sparse signal and low-rank matrix recovery
- AIR tools -- a MATLAB package of algebraic iterative reconstruction methods
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Uncertainty Principles and Signal Recovery
- Practical Optimization
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Linear Inversion of Band-Limited Reflection Seismograms
- A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
- Matching pursuits with time-frequency dictionaries
- The Orthogonal Super Greedy Algorithm and Applications in Compressed Sensing
- Stable signal recovery from incomplete and inaccurate measurements
- Compressed sensing
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