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ICASSP
2008
IEEE
14 years 2 months ago
Compressed sensing - a look beyond linear programming
Recently, significant attention in compressed sensing has been focused on Basis Pursuit, exchanging the cardinality operator with the l1-norm, which leads to a linear formulation...
Christian R. Berger, Javier Areta, Krishna R. Patt...
CORR
2010
Springer
143views Education» more  CORR 2010»
13 years 8 months ago
Compressed Sensing with Coherent and Redundant Dictionaries
This article presents novel results concerning the recovery of signals from undersampled data in the common situation where such signals are not sparse in an orthonormal basis or ...
Emmanuel J. Candès, Yonina C. Eldar, Deanna...
CORR
2008
Springer
234views Education» more  CORR 2008»
13 years 8 months ago
Bayesian Compressive Sensing via Belief Propagation
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-N...
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
TIP
2010
127views more  TIP 2010»
13 years 6 months ago
Bayesian Compressive Sensing Using Laplace Priors
In this paper we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for ...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
ICASSP
2010
IEEE
13 years 2 months ago
Using reed-muller sequences as deterministic compressed sensing matrices for image reconstruction
An image reconstruction algorithm using compressed sensing (CS) with deterministic matrices of second-order ReedMuller (RM) sequences is introduced. The 1D algorithm of Howard et ...
Kangyu Ni, Somantika Datta, Prasun Mahanti, Svetla...