On Theoretical Optimization of the Sensing Matrix for Sparse-Dictionary Signal Recovery
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- Type
- article
- Published
- 2019-11-01
- Cited by
- 1
- References
- 23
- OpenAlex
- https://openalex.org/W2984862312
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:210971020
Keywords
Orthonormal basis, Compressed sensing, Sparse approximation, Basis pursuit, Computer science
References
- Compressed Sensing
- Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity
- Practical approximate projection schemes in greedy signal space methods
- Coherence and RIP Analysis for Greedy Algorithms in Compressive Sensing
- Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit
- Optimal D-RIP bounds in compressed sensing
- Compressed sensing radar
- Sparse Approximate Solutions to Linear Systems
- A Simple Proof of the Restricted Isometry Property for Random Matrices
- Greedy Algorithms for Joint Sparse Recovery
- Generalized Orthogonal Matching Pursuit
- Signal Space CoSaMP for Sparse Recovery With Redundant Dictionaries
- Compressive Coded Aperture Spectral Imaging: An Introduction
- Greedy Signal Space Methods for incoherence and beyond
- Sampling and Reconstructing Signals From a Union of Linear Subspaces
- Decoding by linear programming
- Matching pursuits with time-frequency dictionaries
- Robust Nonnegative Sparse Recovery and the Nullspace Property of 0/1 Measurements
- On the Reconstruction of Block-Sparse Signals With an Optimal Number of Measurements
- GREEDY SIGNAL SPACE METHODS FOR INCOHERENCE AND BEYOND
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