Results 21 to 30 of about 141,696 (278)
On sharp performance bounds for robust sparse signal recoveries [PDF]
, 2009 It is well known in compressive sensing that l_1 minimization can recover the sparsest solution for a large class of underdetermined systems of linear equations, provided the signal is sufficiently sparse.
Hassibi, Babak, Xu, Weiyu
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IEEE Access, 2020 In this paper, an asymptotic bound on the recovery error probability of a sparse signal is derived for the orthogonal matching pursuit algorithm. The proposed bound is based on the support recovery analysis with a random measurement matrix, which gets ...
Yonggu Lee, Jinho Choi, Euiseok Hwang
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Sparse Signal Recovery via ECME Thresholding Pursuits [PDF]
Mathematical Problems in Engineering, 2012 The emerging theory of compressive sensing (CS) provides a new sparse signal processing paradigm for reconstructing sparse signals from the undersampled linear measurements. Recently, numerous algorithms have been developed to solve convex optimization problems for CS sparse signal recovery.
Song, Heping, Wang, Guoli
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Mathematics, 2019 Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory.
Dingfei Jin +3 more
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Adaptive recovery of dictionary-sparse signals using binary measurements
EURASIP Journal on Advances in Signal Processing, 2022 One-bit compressive sensing (CS) is an advanced version of sparse recovery in which the sparse signal of interest can be recovered from extremely quantized measurements. Namely, only the sign of each measure is available to us. The ground-truth signal is
Hossein Beheshti +2 more
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Explicit measurements with almost optimal thresholds for compressed sensing [PDF]
, 2008 We consider the deterministic construction of a measurement matrix and a recovery method for signals that are block sparse. A signal that has dimension N = nd, which consists of n blocks of size d, is called (s, d)-block sparse if only s blocks out ...
Hassibi, Babak, Parvaresh, Farzad
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Signal Space CoSaMP for Sparse Recovery with Redundant Dictionaries [PDF]
, 2012 Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. The bulk of the CS literature has focused on the case where the acquired signal has a sparse or compressible representation in an orthonormal basis.
Davenport, Mark A. +2 more
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Sparse signal recovery with unknown signal sparsity [PDF]
EURASIP Journal on Advances in Signal Processing, 2014 In this paper, we proposed a detection-based orthogonal match pursuit (DOMP) algorithm for compressive sensing. Unlike the conventional greedy algorithm, our proposed algorithm does not rely on the priori knowledge of the signal sparsity, which may not be known for some application, e.g., sparse multipath channel estimation.
Xiong, Wenhui, Cao, Jin, Li, Shaoqian
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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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Variational Bayesian Sparse Signal Recovery With LSM Prior
IEEE Access, 2017 This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang +3 more
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