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ICASSP
2011
IEEE
10 years 9 months ago
Lorentzian based iterative hard thresholding for compressed sensing
In this paper we propose a robust iterative hard thresolding (IHT) algorithm for reconstructing sparse signals in the presence of impulsive noise. To address this problem, we use ...
Rafael E. Carrillo, Kenneth E. Barner
CORR
2008
Springer
144views Education» more  CORR 2008»
11 years 5 months ago
Iterative Hard Thresholding for Compressed Sensing
Compressed sensing is a technique to sample compressible signals below the Nyquist rate, whilst still allowing near optimal reconstruction of the signal. In this paper we present ...
Thomas Blumensath, Mike E. Davies
ICASSP
2011
IEEE
10 years 9 months ago
Iterative hard thresholding for compressed sensing with partially known support
Recent works in modified compressed sensing (CS) show that reconstruction of sparse or compressible signals with partially known support yields better results than traditional CS...
Rafael E. Carrillo, Luisa F. Polania, Kenneth E. B...
ICASSP
2009
IEEE
12 years 10 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
CORR
2010
Springer
97views Education» more  CORR 2010»
11 years 3 months ago
On the Scaling Law for Compressive Sensing and its Applications
1 minimization can be used to recover sufficiently sparse unknown signals from compressed linear measurements. In fact, exact thresholds on the sparsity, as a function of the ratio...
Weiyu Xu, Ao Tang
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