Heavy-Ball-Based Hard Thresholding Pursuit for Sparse Phase Retrieval Problems
Mathematics, 2023 We introduce a novel iterative algorithm, termed the Heavy-Ball-Based Hard Thresholding Pursuit for sparse phase retrieval problem (SPR-HBHTP), to reconstruct a sparse signal from a small number of magnitude-only measurements.
Yingying Li +3 more
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Restricted Isometry Property under High Correlations
CoRR, 2019 30 pages, fixed minor ...
Shiva Prasad Kasiviswanathan +1 more
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RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction
Journal of Advanced Research, 2016 Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted ℓ1 minimization for signal reconstruction which is computationally expensive.
Michael M. Abdel-Sayed +2 more
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Natural Thresholding Algorithms for Signal Recovery With Sparsity
IEEE Open Journal of Signal Processing, 2022 The algorithms based on the technique of optimal $k$-thresholding (OT) were recently proposed for signal recovery, and they are very different from the traditional family of hard thresholding methods.
Yun-Bin Zhao, Zhi-Quan Luo
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Hierarchical restricted isometry property for Kronecker product measurements [PDF]
2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2018 5 ...
Ingo Roth +4 more
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A Simple Proof of the Restricted Isometry Property for Random Matrices [PDF]
Constructive Approximation, 2008 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Baraniuk, Richard +3 more
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A Near-Optimal Restricted Isometry Condition of Multiple Orthogonal Least Squares
IEEE Access, 2019 In this paper, we analyze the performance guarantee of multiple orthogonal least squares (MOLS) in recovering sparse signals. Specifically, we show that the MOLS algorithm ensures the accurate recovery of any K-sparse signal, provided that a sampling ...
Junhan Kim, Byonghyo Shim
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On the strong restricted isometry property of Bernoulli random matrices [PDF]
Journal of Approximation Theory, 2019 The study of the restricted isometry property (RIP) of corrupted random matrices is particularly important in the field of compressed sensing (CS) with corruptions. If a matrix still satisfies the RIP after that a certain portion of rows are erased, then we say that this matrix has the strong restricted isometry property (SRIP).
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Non-convex block-sparse compressed sensing with coherent tight frames
EURASIP Journal on Advances in Signal Processing, 2020 In this paper, we present a non-convex ℓ 2/ℓ q ...
Xiaohu Luo +4 more
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A Novel Sparsity Adaptive Algorithm for Underwater Acoustic Signal Reconstruction
Sensors, 2022 In view of the fact that most of the traditional algorithms for reconstructing underwater acoustic signals from low-dimensional compressed data are based on known sparsity, a sparsity adaptive and variable step-size matching pursuit (SAVSMP) algorithm is
Na Li, Xinghui Yin, Haitao Li
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