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CORR
2008
Springer
234views Education» more  CORR 2008»
13 years 11 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 9 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 5 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...
INTERSPEECH
2010
13 years 5 months ago
Towards a robust face recognition system using compressive sensing
An application of compressive sensing (CS) theory in imagebased robust face recognition is considered. Most contemporary face recognition systems suffer from limited abilities to ...
Allen Y. Yang, Zihan Zhou, Yi Ma, Shankar Sastry
TIT
2010
174views Education» more  TIT 2010»
13 years 5 months ago
Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation
Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. In essence, CS enables the recovery of high-dimensional sparse signals from relatively few ...
Jarvis Haupt, Waheed Uz Zaman Bajwa, Gil M. Raz, R...