The Experts below are selected from a list of 73134 Experts worldwide ranked by ideXlab platform
Thomas Strohmer - One of the best experts on this subject based on the ideXlab platform.
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High-resolution radar via Compressed Sensing
IEEE Transactions on Signal Processing, 2009Co-Authors: Matthew A. Herman, Thomas StrohmerAbstract:A stylized Compressed Sensing radar is proposed in which the time-frequency plane is discretized into an N by N grid. Assuming the number of targets K is small (i.e., K much less than N^2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of Compressed Sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel Compressed Sensing approach offers great potential for better resolution over classical radar.
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Compressed Sensing radar
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings, 2008Co-Authors: Matthew Herman, Thomas StrohmerAbstract: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. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. By comparing traditional uncertainty principles with those of Compressed Sensing, this novel approach reveals great potential for better resolution over classical radar.
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ICASSP - Compressed Sensing radar
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Matthew A. Herman, Thomas StrohmerAbstract:A stylized Compressed Sensing radar is proposed in which the time- frequency plane is discretized into an N times N grid. Assuming the number of targets K is small (i.e., K Lt N2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of Compressed Sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel Compressed Sensing approach offers great potential for better resolution over classical radar.
Pierre Vandergheynst - One of the best experts on this subject based on the ideXlab platform.
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Compressed Sensing and Redundant Dictionaries
IEEE Transactions on Information Theory, 2008Co-Authors: Holger Rauhut, Karin Schnass, Pierre VandergheynstAbstract:This paper extends the concept of Compressed Sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a deterministic dictionary, has small restricted isometry constants. Thus, signals that are sparse with respect to the dictionary can be recovered via basis pursuit (BP) from a small number of random measurements. Further, thresholding is investigated as recovery algorithm for Compressed Sensing, and conditions are provided that guarantee reconstruction with high probability. The different schemes are compared by numerical experiments.
Matthew A. Herman - One of the best experts on this subject based on the ideXlab platform.
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High-resolution radar via Compressed Sensing
IEEE Transactions on Signal Processing, 2009Co-Authors: Matthew A. Herman, Thomas StrohmerAbstract:A stylized Compressed Sensing radar is proposed in which the time-frequency plane is discretized into an N by N grid. Assuming the number of targets K is small (i.e., K much less than N^2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of Compressed Sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel Compressed Sensing approach offers great potential for better resolution over classical radar.
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ICASSP - Compressed Sensing radar
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Matthew A. Herman, Thomas StrohmerAbstract:A stylized Compressed Sensing radar is proposed in which the time- frequency plane is discretized into an N times N grid. Assuming the number of targets K is small (i.e., K Lt N2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of Compressed Sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel Compressed Sensing approach offers great potential for better resolution over classical radar.
John M Pauly - One of the best experts on this subject based on the ideXlab platform.
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Compressed Sensing from research to clinical practice with data driven learning
arXiv: Image and Video Processing, 2019Co-Authors: Joseph Y Cheng, Feiyu Chen, Christopher M Sandino, Morteza Mardani, John M Pauly, Shreyas S VasanawalaAbstract:Compressed Sensing in MRI enables high subsampling factors while maintaining diagnostic image quality. This technique enables shortened scan durations and/or improved image resolution. Further, Compressed Sensing can increase the diagnostic information and value from each scan performed. Overall, Compressed Sensing has significant clinical impact in improving the diagnostic quality and patient experience for imaging exams. However, a number of challenges exist when moving Compressed Sensing from research to the clinic. These challenges include hand-crafted image priors, sensitive tuning parameters, and long reconstruction times. Data-driven learning provides a solution to address these challenges. As a result, Compressed Sensing can have greater clinical impact. In this tutorial, we will review the Compressed Sensing formulation and outline steps needed to transform this formulation to a deep learning framework. Supplementary open source code in python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying data-driven Compressed Sensing in the clinical setting.
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Compressed Sensing mri
IEEE Signal Processing Magazine, 2008Co-Authors: Michael Lustig, David L Donoho, Juan M Santos, John M PaulyAbstract: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. There is also an emphasis on the understanding of the driving factors in applications, including limitations imposed by MRI hardware, by the characteristics of different types of images, and by clinical concerns.
Holger Rauhut - One of the best experts on this subject based on the ideXlab platform.
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Compressed Sensing and Redundant Dictionaries
IEEE Transactions on Information Theory, 2008Co-Authors: Holger Rauhut, Karin Schnass, Pierre VandergheynstAbstract:This paper extends the concept of Compressed Sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a deterministic dictionary, has small restricted isometry constants. Thus, signals that are sparse with respect to the dictionary can be recovered via basis pursuit (BP) from a small number of random measurements. Further, thresholding is investigated as recovery algorithm for Compressed Sensing, and conditions are provided that guarantee reconstruction with high probability. The different schemes are compared by numerical experiments.