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IEEE Transactions on Image Processing, 2012
Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement ...
Marco F, Duarte, Richard G, Baraniuk
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Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement ...
Marco F, Duarte, Richard G, Baraniuk
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Accelerating SENSE using compressed sensing
Magnetic Resonance in Medicine, 2009AbstractBoth parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for ...
Dong, Liang +3 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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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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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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Demosaicking with compressive sensing
2012 20th Signal Processing and Communications Applications Conference (SIU), 2012Sparse signals can be recovered with less number of measurements compared to standard methods using Compressive Sensing (CS) theory. In digital cameras, color filter arrays (CFA) are used to sample each color band with less measurements than the normal. The color images are reconstructed using interpolation of measured pixel values.
Gürbüz, A. C., İlbeği, H.
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2012
Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining ...
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Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining ...
openaire +1 more source