High-order block RIP for nonconvex block-sparse compressed sensing
From MaRDI portal
Publication:6583081
DOI10.1515/jiip-2022-0017zbMath1544.94172MaRDI QIDQ6583081
Jingyao Hou, Jian-Jun Wang, Feng Zhang, Jin-ping Jia, Xin-Ling Liu, Jian-Wen Huang
Publication date: 6 August 2024
Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Sampling theory in information and communication theory (94A20)
Cites Work
- Block sparse recovery via mixed \(l_2/l_1\) minimization
- On the null space property of \(l_q\)-minimization for \(0 < q \leq 1\) in compressed sensing
- Restricted \(p\)-isometry property and its application for nonconvex compressive sensing
- Uniform recovery of fusion frame structured sparse signals
- Fast implementation of orthogonal greedy algorithm for tight wavelet frames
- A simple proof of the restricted isometry property for random matrices
- Compressed sensing of color images
- Perturbation analysis of \(L_{1-2}\) method for robust sparse recovery
- Group sparse recovery in impulsive noise via alternating direction method of multipliers
- A perturbation analysis based on group sparse representation with orthogonal matching pursuit
- Sharp RIP bound for sparse signal and low-rank matrix recovery
- Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization
- Sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit
- A new bound on the block restricted isometry constant in compressed sensing
- Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
- Restricted isometry properties and nonconvex compressive sensing
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- On the Reconstruction of Block-Sparse Signals With an Optimal Number of Measurements
- Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
- Block-Sparse Recovery via Convex Optimization
- Robust Sparse Recovery in Impulsive Noise via $\ell _p$ -$\ell _1$ Optimization
- Limits on Sparse Data Acquisition: RIC Analysis of Finite Gaussian Matrices
- On the Gap Between Restricted Isometry Properties and Sparse Recovery Conditions
- Robust Recovery of Signals From a Structured Union of Subspaces
- Recovery analysis for block ℓp − ℓ1 minimization with prior support information
- Uniform RIP Conditions for Recovery of Sparse Signals by $\ell _p\,(0< p\leq 1)$ Minimization
- The High Order Block RIP Condition for Signal Recovery
- Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
- Weighted lp − l1 minimization methods for block sparse recovery and rank minimization
- Compressed sensing
- A general theory of concave regularization for high-dimensional sparse estimation problems
This page was built for publication: High-order block RIP for nonconvex block-sparse compressed sensing