8 search results - page 1 / 2 » Fast bayesian compressive sensing using Laplace priors |
ICASSP
2009
IEEE
13 years 11 months ago
2009
IEEE
In this paper we model the components of the compressive sensing (CS) problem using the Bayesian framework by utilizing a hierarchical form of the Laplace prior to model sparsity ...
TIP
2010
13 years 2 months ago
2010
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 ...
JMLR
2008
13 years 4 months ago
2008
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
SCALESPACE
2007
Springer
13 years 10 months ago
2007
Springer
This paper proposes an extension of compressed sensing that allows to express the sparsity prior in a dictionary of bases. This enables the use of the random sampling strategy of c...
CORR
2008
Springer
13 years 4 months ago
2008
Springer
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...