Results 21 to 30 of about 1,669,292 (291)
Identification of Matrices Having a Sparse Representation [PDF]
, 2008 We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary.
Pfander, Goetz E. +6 more
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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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High-resolution Sparse Self-calibration Imaging for Vortex Radar with Phase Error
Leida xuebao, 2021 The Orbital Angular Momentum (OAM)-based vortex radar has drawn increasing attention because of its potential for high-resolution imaging. The vortex radar high resolution imaging with limited OAM modes is commonly solved by sparse recovery, in which the
Haiyou QU +3 more
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New Directions In Sparse Sampling and Estimation For Underdetermined Systems [PDF]
, 2013 A central objective in signal processing is to infer meaningful information from a set of measurements or data. While most signal models have an overdetermined structure (the number of unknowns less than the number of equations), traditionally very few ...
Piya Pal, Pal, Piya
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Low-Complexity DCD-Based Sparse Recovery Algorithms
IEEE Access, 2017 Sparse recovery techniques find applications in many areas. Real-time implementation of such techniques has been recently an important area for research.
Yuriy V. Zakharov +3 more
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Compressed Sensing of Extracellular Neurophysiology Signals: A Review
Frontiers in Neuroscience, 2021 This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless ...
Biao Sun, Wenfeng Zhao
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Sparse Signal Recovery via Exponential Metric Approximation
Tsinghua Science and Technology, 2017 Sparse signal recovery problems are common in parameter estimation, image processing, pattern recognition, and so on. The problem of recovering a sparse signal representation from a signal dictionary might be classified as a linear constraint ℓ0 ...
Jian Pan, Jun Tang, Wei Zhu
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Recovery of Sparsely Corrupted Signals [PDF]
IEEE Transactions on Information Theory, 2012 We investigate the recovery of signals exhibiting a sparse representation in a general (i.e., possibly redundant or incomplete) dictionary that are corrupted by additive noise admitting a sparse representation in another general dictionary. This setup covers a wide range of applications, such as image inpainting, super-resolution, signal separation ...
Christoph Studer +3 more
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An ALPS view of sparse recovery [PDF]
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 We provide two compressive sensing (CS) recovery algorithms based on iterative hard-thresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra of Nesterov's optimal gradient methods.
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Sparse recovery for discrete tomography [PDF]
2010 IEEE International Conference on Image Processing, 2010 Discrete tomography (DT) focuses on the reconstruction of a discrete valued image from few projection angles. Prior knowledge about the image can greatly increase the quality of the reconstructed image, especially when a small number of projections are available. In this paper, we show that DT can be formulated as a sparse signal recovery problem.
Yen-ting Lin +2 more
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