Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication

DOI10.1007/978-3-319-16042-9_10zbMath1333.94019arXiv1404.0218OpenAlexW1866218998MaRDI QIDQ3460837

Publication date: 8 January 2016

Published in: Compressed Sensing and its Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.0218

Mathematics Subject Classification ID

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

Related Items (1)

On the stability of sparse convolutions

Cites Work

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On the stability of sparse convolutions
Restricted isometries for partial random circulant matrices
Perturbations of measurement matrices and dictionaries in compressed sensing
Compressed sensing with coherent and redundant dictionaries
Random projections of smooth manifolds
A simple proof of the restricted isometry property for random matrices
The convex geometry of linear inverse problems
Structural additive theory. Based on courses given at Karl-Franzens-Universität Graz, Austria, 2008--2012
Sharp RIP bound for sparse signal and low-rank matrix recovery
Saving phase: injectivity and stability for phase retrieval
Simultaneous analysis of Lasso and Dantzig selector
RIPless compressed sensing from anisotropic measurements
Phase retrieval: stability and recovery guarantees
Suprema of Chaos Processes and the Restricted Isometry Property
Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices
Blind Deconvolution Using Convex Programming
Reconstruction From Anisotropic Random Measurements
New and Improved Johnson–Lindenstrauss Embeddings via the Restricted Isometry Property
Biquadratic Optimization Over Unit Spheres and Semidefinite Programming Relaxations
Compressed Sensing and Redundant Dictionaries
Compressive Sensing by Random Convolution
Near-Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices Via Sparse Power Factorization
Sampling and Recovery of Pulse Streams
Sensitivity to Basis Mismatch in Compressed Sensing
Combinatorics and linear algebra of Freiman's isomorphism
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
Quasi-linear Compressed Sensing
Blind Compressed Sensing
Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
Spectral Properties of Banded Toeplitz Matrices

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