Robust signal recovery for ℓ 1–2 minimization via prior support information
From MaRDI portal
Publication:5860797
DOI10.1088/1361-6420/ac274azbMath1479.94078OpenAlexW3200762276MaRDI QIDQ5860797
Shuguang Zhang, Jing Zhang, Wendong Wang
Publication date: 23 November 2021
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1088/1361-6420/ac274a
Nonconvex programming, global optimization (90C26) Computing methodologies for image processing (68U10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Cites Work
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- New bounds on the restricted isometry constant \(\delta _{2k}\)
- The restricted isometry property and its implications for compressed sensing
- Recovery of signals under the condition on RIC and ROC via prior support information
- Sparse signal recovery with prior information by iterative reweighted least squares algorithm
- A short note on compressed sensing with partially known signal support
- Sharp RIP bound for sparse signal and low-rank matrix recovery
- Recovery analysis for weighted \(\ell_{1}\)-minimization using the null space property
- Sparse Approximation using $\ell_1-\ell_2$ Minimization and Its Application to Stochastic Collocation
- Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- A Proof of Conjecture on Restricted Isometry Property Constants $\delta _{tk}\ \left(0<t<\frac {4}{3}\right)$
- Modified-CS: Modifying Compressive Sensing for Problems With Partially Known Support
- Compressed Sensing and Affine Rank Minimization Under Restricted Isometry
- Weighted ${\ell}_{{1}}$-minimization for sparse recovery under arbitrary prior information
- ℓ 1 − αℓ 2 minimization methods for signal and image reconstruction with impulsive noise removal
- Robust recovery of signals with partially known support information using weighted BPDN
- Sparse Recovery via Partial Regularization: Models, Theory, and Algorithms
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- Recovering Compressively Sampled Signals Using Partial Support Information
- Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
- Sparse Signal Reconstruction via Iterative Support Detection
- Compressed sensing