Results 21 to 30 of about 44,247 (253)
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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ORTP: A Video SAR Imaging Algorithm Based on Low-Tubal-Rank Tensor Recovery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day.
Wei Pu +3 more
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Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning
Sensors, 2022 In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method.
Yanbin Zhang +4 more
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Low-rank matrix recovery from errors and erasures [PDF]
2011 IEEE International Symposium on Information Theory Proceedings, 2011 27 pages, 3 figures.
Yudong Chen 0001 +3 more
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IEEE Access, 2022 Recent advances have shown that the challenging problem of matrix completion arises from real-world applications, such as image recovery, and recommendation systems.
Ying Zhang +3 more
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Recovery of High Order Statistics of PSK Signals Based on Low-Rank Matrix Completion
IEEE Access, 2023 High order statistics are useful for automatic modulation recognition and parameter estimations. In this paper, we cast the problem of recovering high order statistics of PSK signals taken from nonuniform compressive samples as a one of recovering a low ...
Zhengli Xing +4 more
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s-Goodness for Low-Rank Matrix Recovery
Abstract and Applied Analysis, 2013 Low-rank matrix recovery (LMR) is a rank minimization problem subject to linear equality constraints, and it arises in many fields such as signal and image processing, statistics, computer vision, and system identification and control.
Lingchen Kong +2 more
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Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion
Remote Sensing, 2022 Electromagnetic data annotation is one of the most important steps in many signal processing applications, e.g., radar signal deinterleaving and radar mode analysis.
Wei Zhang +5 more
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Proof Methods for Robust Low-Rank Matrix Recovery
, 2022 Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse problems, such as matrix completion, blind deconvolution, and phase retrieval. Over the last two decades, a number of works have rigorously analyzed the reconstruction performance for such scenarios, giving rise to a rather general understanding of the ...
Fuchs, Tim +5 more
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Nonconvex Low Tubal Rank Tensor Minimization
IEEE Access, 2019 In the sparse vector recovery problem, the L0-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a ...
Yaru Su, Xiaohui Wu, Genggeng Liu
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