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ICASSP
2011
IEEE
12 years 9 months ago
Bayesian Compressive Sensing for clustered sparse signals
In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. ...
Lei Yu, Hong Sun, Jean-Pierre Barbot, Gang Zheng
TIT
2010
100views Education» more  TIT 2010»
13 years 16 days ago
Theoretical and empirical results for recovery from multiple measurements
The joint-sparse recovery problem aims to recover, from sets of compressed measurements, unknown sparse matrices with nonzero entries restricted to a subset of rows. This is an ex...
Ewout van den Berg, Michael P. Friedlander
ICASSP
2011
IEEE
12 years 9 months ago
Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals
In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly spa...
Shubha Shedthikere, Ananthanarayanan Chockalingam
ICASSP
2011
IEEE
12 years 9 months ago
TLS-FOCUSS for sparse recovery with perturbed dictionary
In this paper, we propose a new algorithm, TLS-FOCUSS, for sparse recovery for large underdetermined linear systems, based on total least square (TLS) method and FOCUSS(FOCal Unde...
Xuebing Han, Hao Zhang, Huadong Meng
CORR
2011
Springer
183views Education» more  CORR 2011»
13 years 9 days ago
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
— We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally corre...
Zhilin Zhang, Bhaskar D. Rao