Results 21 to 30 of about 794 (157)
On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit
IEEE Access, 2019 Compressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been ...
Xiaobo Zhang +4 more
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On the Certification of the Restricted Isometry Property
CoRR, 2011 Compressed sensing is a technique for finding sparse solutions to underdetermined linear systems. This technique relies on properties of the sensing matrix such as the restricted isometry property. Sensing matrices that satisfy the restricted isometry property with optimal parameters are mainly obtained via probabilistic arguments.
Pascal Koiran, Anastasios Zouzias
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Restricted p-Isometry Properties of Partially Sparse Signal Recovery
Discrete Dynamics in Nature and Society, 2013 By generalizing the restricted p-isometry property to the partially sparse signal recovery problem, we give a sufficient condition for exactly recovering partially sparse signal via the partial lp minimization (truncated lp minimization) problem with p ...
Haini Bi, Lingchen Kong, Naihua Xiu
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A New Nonconvex Sparse Recovery Method for Compressive Sensing
Frontiers in Applied Mathematics and Statistics, 2019 As an extension of the widely used ℓr-minimization with 0 < r ≤ 1, a new non-convex weighted ℓr − ℓ1 minimization method is proposed for compressive sensing.
Zhiyong Zhou, Jun Yu
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A Sharp RIP Condition for Orthogonal Matching Pursuit
Abstract and Applied Analysis, 2013 A restricted isometry property (RIP) condition δK+KθK ...
Wei Dan
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Efficiency of Orthogonal Matching Pursuit for Group Sparse Recovery
Axioms, 2023 We propose the Group Orthogonal Matching Pursuit (GOMP) algorithm to recover group sparse signals from noisy measurements. Under the group restricted isometry property (GRIP), we prove the instance optimality of the GOMP algorithm for any decomposable ...
Chunfang Shao +3 more
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IEEE Access, 2019 In this paper, we study the minimization of lp-q (0 <; p ≤ 1, q ≥ 1, p=6q), the general difference of lp and lq norms/quasi-norms, as a nonconvex metric for solving unconstrained nonlinear programming. We first establish an exact (stable)
Yi Cen, Linna Zhang, Ke Wang, Yigang Cen
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Instance Optimal Decoding and the Restricted Isometry Property
Journal of Physics: Conference Series, 2018 In this paper, we address the question of information preservation in ill-posed, non-linear inverse problems, assuming that the measured data is close to a low-dimensional model set. We provide necessary and sufficient conditions for the existence of a so-called instance optimal decoder, i.e., that is robust to noise and modelling error.
Keriven, Nicolas, Gribonval, Rémi
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Improved RIP Conditions for Compressed Sensing with Coherent Tight Frames
Discrete Dynamics in Nature and Society, 2017 This paper establishes new sufficient conditions on the restricted isometry property (RIP) for compressed sensing with coherent tight frames.
Yao Wang, Jianjun Wang
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Stable Recovery of Signals From Highly Corrupted Measurements
IEEE Access, 2018 In this paper, we consider the stable recovery of sparse or proximately sparse signals x ∈ ℝn from highly corrupted linear measurements b = Ax + f + e, where f ∈ ℝm is a sparse error vector whose nonzero entries may be ...
Ningning Li, Wengu Chen, Peng Li
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