Results 11 to 20 of about 31,241 (250)
Achieving Robust Compressive Sensing Seismic Acquisition with a Two-Step Sampling Approach [PDF]
Sensors, 2023 The compressive sensing (CS) framework offers a cost-effective alternative to dense alias-free sampling. Designing seismic layouts based on the CS technique imposes the use of specific sampling patterns in addition to the logistical and geophysical ...
Anna Titova +2 more
doaj +2 more sources
A Remark on the Restricted Isometry Property in Orthogonal Matching Pursuit [PDF]
IEEE Transactions on Information Theory, 2012 This paper demonstrates that if the restricted isometry constant $δ_{K+1}$ of the measurement matrix $A$ satisfies $$ δ_{K+1} < \frac{1}{\sqrt{K}+1}, $$ then a greedy algorithm called Orthogonal Matching Pursuit (OMP) can recover every $K$--sparse signal $\mathbf{x}$ in $K$ iterations from $A\x$.
Yi Shen
exaly +3 more sources
Incoherent dictionaries and the statistical restricted isometry property [PDF]
CoRR, 2008 In this article we present a statistical version of the Candes-Tao restricted isometry property (SRIP for short) which holds in general for any incoherent dictionary which is a disjoint union of orthonormal bases.
Gurevich, Shamgar, Hadani, Ronny
core +6 more sources
Compressed Sensing: How Sharp Is the Restricted Isometry Property? [PDF]
SIAM Review, 2011 Compressed Sensing (CS) seeks to recover an unknown vector with $N$ entries by making far fewer than $N$ measurements; it posits that the number of compressed sensing measurements should be comparable to the information content of the vector, not simply $N$. CS combines the important task of compression directly with the measurement task.
Jeffrey D Blanchard +2 more
exaly +7 more sources
Analysis of Orthogonal Matching Pursuit Using the Restricted Isometry Property [PDF]
IEEE Transactions on Information Theory, 2010 Orthogonal Matching Pursuit (OMP) is the canonical greedy algorithm for sparse approximation. In this paper we demonstrate that the restricted isometry property (RIP) can be used for a very straightforward analysis of OMP. Our main conclusion is that the RIP of order $K+1$ (with isometry constant $δ< \frac{1}{3\sqrt{K}}$) is sufficient for OMP to ...
Mark A Davenport, Michael B Wakin
exaly +3 more sources
The restricted isometry property for random block diagonal matrices
Applied and Computational Harmonic Analysis, 2015 In Compressive Sensing, the Restricted Isometry Property (RIP) ensures that robust recovery of sparse vectors is possible from noisy, undersampled measurements via computationally tractable algorithms. It is by now well-known that Gaussian (or, more generally, sub-Gaussian) random matrices satisfy the RIP under certain conditions on the number of ...
Han Lun Yap +2 more
exaly +5 more sources
The Restricted Isometry Property for Measurements from Group Orbits
2025 International Conference on Sampling Theory and Applications (SampTA)2025 International Conference on Sampling Theory and Applications (SampTA) : [Proceedings] 15. International Conference on Sampling Theory and Applications, SampTA 2025, Vienna, Austria, 28 Jul 2025 - 1 Aug 2025; IEEE 5 Seiten (2025).
Führ, Hartmut, Gilles, Timm
exaly +4 more sources
A note on orthogonal matching pursuit under restricted isometry property
IET Signal Processing, 2022 The orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. The number of iterations required for the OMP algorithm to perform exact the recovery of sparse signals is a fundamental problem in signal ...
Xueping Chen +3 more
doaj +1 more source
On the restricted isometry property of the Paley matrix [PDF]
Linear Algebra and its Applications, 2021 12 pages.
openaire +3 more sources
Linear transformations and Restricted Isometry Property [PDF]
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009 The Restricted Isometry Property (RIP) introduced by Candés and Tao is a fundamental property in compressed sensing theory. It says that if a sampling matrix satisfies the RIP of certain order proportional to the sparsity of the signal, then the original signal can be reconstructed even if the sampling matrix provides a sample vector which is much ...
Leslie Ying, Yi Ming Zou
openaire +2 more sources