Results 11 to 20 of about 359 (123)
Intelligent Systems with Applications, 2022 Previous works in low-rank matrix recovery literature focused on the study of the partially perturbed low-rank matrix recovery model y=A(X)+ω, where y∈RM is the observed vector, A:Rm×n→RM is the measurement operator, X∈Rm×n is the matrix to be recovered,
Zahia Aidene +2 more
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Probability of correct reconstruction in compressive spectral imaging
Ingeniería e Investigación, 2016 Coded Aperture Snapshot Spectral Imaging (CASSI) systems capture the 3-dimensional (3D) spatio-spectral information of a scene using a set of 2-dimensional (2D) random coded Focal Plane Array (FPA) measurements.
Samuel Eduardo Pinilla +2 more
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Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements
Journal of Applied Mathematics, 2013 The principal component prsuit with reduced linear measurements (PCP_RLM) has gained great attention in applications, such as machine learning, video, and aligning multiple images.
Qingshan You, Qun Wan, Haiwen Xu
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Stable Sparse Model with Non-Tight Frame
Applied Sciences, 2020 Overcomplete representation is attracting interest in image restoration due to its potential to generate sparse representations of signals. However, the problem of seeking sparse representation must be unstable in the presence of noise.
Min Zhang, Yunhui Shi, Na Qi, Baocai Yin
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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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Efficiency of orthogonal super greedy algorithm under the restricted isometry property
Journal of Inequalities and Applications, 2019 We investigate the efficiency of orthogonal super greedy algorithm (OSGA) for sparse recovery and approximation under the restricted isometry property (RIP).
Xiujie Wei, Peixin Ye
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Multichannel compressive sensing MRI using noiselet encoding. [PDF]
PLoS ONE, 2015 The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS).
Kamlesh Pawar, Gary Egan, Jingxin Zhang
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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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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
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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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