RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction

DOI10.1016/J.NEUNET.2014.10.010zbMath1361.94026OpenAlexW2012881376WikidataQ41700213 ScholiaQ41700213MaRDI QIDQ889379

Deepak Mishra, V. Vivekanand, U. Shyamkumar, L. Vidya

Publication date: 6 November 2015

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2014.10.010

zbMATH Keywords

convergence rateradial basis functioncompressed sensingmeasurement matrix

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (1)

Framework for segmented threshold \(\ell_0\) gradient approximation based network for sparse signal recovery

Uses Software

• PDCO
• CoSaMP
• L1-MAGIC

Cites Work

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• The restricted isometry property and its implications for compressed sensing
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• A simple proof of the restricted isometry property for random matrices
• Deterministic constructions of compressed sensing matrices
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Sparse and Redundant Representations
• Atomic Decomposition by Basis Pursuit
• A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
• Sampling and Recovery of Pulse Streams
• Sparsity and incoherence in compressive sampling
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing

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