Results 271 to 280 of about 1,002,666 (308)
Some of the next articles are maybe not open access.
IEEE Transactions on Information Theory, 2006
Summary: Suppose \(x\) is an unknown vector in \(\mathbb R^m\) (a digital image or signal); we plan to measure \(n\) general linear functionals of \(x\) and then reconstruct. If \(x\) is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements \(n\) can ...
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Summary: Suppose \(x\) is an unknown vector in \(\mathbb R^m\) (a digital image or signal); we plan to measure \(n\) general linear functionals of \(x\) and then reconstruct. If \(x\) is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements \(n\) can ...
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IEEE Signal Processing Magazine, 2008
This article reviews the requirements for successful compressed sensing (CS), describes their natural fit to MRI, and gives examples of four interesting applications of CS in MRI. The authors emphasize on an intuitive understanding of CS by describing the CS reconstruction as a process of interference cancellation.
Michael Lustig +3 more
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This article reviews the requirements for successful compressed sensing (CS), describes their natural fit to MRI, and gives examples of four interesting applications of CS in MRI. The authors emphasize on an intuitive understanding of CS by describing the CS reconstruction as a process of interference cancellation.
Michael Lustig +3 more
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2013 IEEE International Conference on Image Processing, 2013
Compressive sensing allows us to recover signals that are linearly sparse in some basis from a smaller number of measurements than traditionally required. However, it has been shown that many classes of images or video can be more efficiently modeled as lying on a nonlinear manifold, and hence described as a non-linear function of a few underlying ...
Farhad Pourkamali-Anaraki +1 more
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Compressive sensing allows us to recover signals that are linearly sparse in some basis from a smaller number of measurements than traditionally required. However, it has been shown that many classes of images or video can be more efficiently modeled as lying on a nonlinear manifold, and hence described as a non-linear function of a few underlying ...
Farhad Pourkamali-Anaraki +1 more
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2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N by N grid. Assuming that the number of targets K is small (i.e. KLtN2), then we can transmit a sufficiently ldquoincoherentrdquo pulse and employ the techniques of compressed sensing to reconstruct the target scene.
Matthew A. Herman, Thomas Strohmer
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A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N by N grid. Assuming that the number of targets K is small (i.e. KLtN2), then we can transmit a sufficiently ldquoincoherentrdquo pulse and employ the techniques of compressed sensing to reconstruct the target scene.
Matthew A. Herman, Thomas Strohmer
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2017
We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert +2 more
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We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert +2 more
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Circuits, Systems, and Signal Processing, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
The effect of off-grid atoms has become the prominent problem in application of the Compressed Sensing (CS) techniques to the cases where there is an underlying continuous parametrization. In this work, we develop a generalizing CS framework which shows that sampling to a finite grid is not necessary toward compressive estimation.
Ashkan Panahi, Mats Viberg
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The effect of off-grid atoms has become the prominent problem in application of the Compressed Sensing (CS) techniques to the cases where there is an underlying continuous parametrization. In this work, we develop a generalizing CS framework which shows that sampling to a finite grid is not necessary toward compressive estimation.
Ashkan Panahi, Mats Viberg
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2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
A traditional assumption underlying most data converters is that the signal should be sampled at a rate exceeding twice the highest frequency. Practical signals often posses a sparse structure so that a large part of the bandwidth is not exploited. In this paper, we consider a framework for utilizing this sparsity in order to sample such analog signals
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A traditional assumption underlying most data converters is that the signal should be sampled at a rate exceeding twice the highest frequency. Practical signals often posses a sparse structure so that a large part of the bandwidth is not exploited. In this paper, we consider a framework for utilizing this sparsity in order to sample such analog signals
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2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
In this paper we present a novel compressed sensing (CS) algorithm for the recovery of compressible, possibly time-varying, signal from a sequence of noisy observations. The newly derived scheme is based on the acclaimed unscented Kalman filter (UKF), and is essentially self reliant in the sense that no peripheral optimization or CS algorithm is ...
Avishy Carmi +2 more
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In this paper we present a novel compressed sensing (CS) algorithm for the recovery of compressible, possibly time-varying, signal from a sequence of noisy observations. The newly derived scheme is based on the acclaimed unscented Kalman filter (UKF), and is essentially self reliant in the sense that no peripheral optimization or CS algorithm is ...
Avishy Carmi +2 more
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Rethinking Compressive Sensing
2018 26th European Signal Processing Conference (EUSIPCO), 2018In this paper we show that Compressive Sensing (CS) can be casted as an impulse response estimation problem. Using this interpretation we re-obtain some theoretical results of CS in a simple manner. Moreover, we prove that in the case of a randomly generated sensing matrix, reconstruction probability depends on the kurtosis of the distribution used for
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