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ICASSP
2009
IEEE
14 years 2 months ago
Real-time dynamic MR image reconstruction using Kalman Filtered Compressed Sensing
In recent work, Kalman Filtered Compressed Sensing (KF-CS) was proposed to causally reconstruct time sequences of sparse signals, from a limited number of “incoherent” measure...
Chenlu Qiu, Wei Lu, Namrata Vaswani
ICASSP
2008
IEEE
14 years 2 months ago
Compressive sensing of parameterized shapes in images
Compressive Sensing (CS) uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals. The Hough t...
Ali Cafer Gurbuz, James H. McClellan, Justin K. Ro...
ICASSP
2011
IEEE
12 years 11 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...
CORR
2010
Springer
166views Education» more  CORR 2010»
13 years 8 months ago
The dynamics of message passing on dense graphs, with applications to compressed sensing
`Approximate message passing' algorithms proved to be extremely effective in reconstructing sparse signals from a small number of incoherent linear measurements. Extensive num...
Mohsen Bayati, Andrea Montanari
DCC
2010
IEEE
14 years 2 months ago
On the Systematic Measurement Matrix for Compressed Sensing in the Presence of Gross Errors
Inspired by syndrome source coding using linear error-correcting codes, we explore a new form of measurement matrix for compressed sensing. The proposed matrix is constructed in t...
Zhi Li, Feng Wu, John Wright