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ICASSP
2008
IEEE
16 years 9 days ago
Compressive sensing of parameterized shapes in images
Compressive Sensing (CS) uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals. The Hough t...
Ali Cafer Gurbuz, James H. McClellan, Justin K. Ro...
CORR
2010
Springer
166views Education» more  CORR 2010»
15 years 6 months ago
The dynamics of message passing on dense graphs, with applications to compressed sensing
`Approximate message passing' algorithms proved to be extremely effective in reconstructing sparse signals from a small number of incoherent linear measurements. Extensive num...
Mohsen Bayati, Andrea Montanari
ISBI
2009
IEEE
16 years 20 days ago
Fast Algorithms for Nonconvex Compressive Sensing: MRI Reconstruction from Very Few Data
Compressive sensing is the reconstruction of sparse images or signals from very few samples, by means of solving a tractable optimization problem. In the context of MRI, this can ...
Rick Chartrand
ICASSP
2009
IEEE
16 years 18 days ago
Field inversion by consensus and compressed sensing
— We study the inversion of a random field from pointwise measurements collected by a sensor network. We assume that the field has a sparse representation in a known basis. To ...
Aurora Schmidt, José M. F. Moura
154
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ICASSP
2009
IEEE
16 years 18 days ago
A simple, efficient and near optimal algorithm for compressed sensing
When sampling signals below the Nyquist rate, efficient and accurate reconstruction is nevertheless possible, whenever the sampling system is well behaved and the signal is well ...
Thomas Blumensath, Mike E. Davies