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» Reconstructing sparse signals from their zero crossings
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CORR
2010
Springer
128views Education» more  CORR 2010»
13 years 6 months ago
Blind Compressed Sensing
The fundamental principle underlying compressed sensing is that a signal, which is sparse under some basis representation, can be recovered from a small number of linear measuremen...
Sivan Gleichman, Yonina C. Eldar
ICASSP
2009
IEEE
14 years 1 months ago
Distributed compressive video sensing
Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the...
Li-Wei Kang, Chun-Shien Lu
CVPR
2008
IEEE
14 years 8 months ago
An efficient algorithm for compressed MR imaging using total variation and wavelets
Compressed sensing, an emerging multidisciplinary field involving mathematics, probability, optimization, and signal processing, focuses on reconstructing an unknown signal from a...
Shiqian Ma, Wotao Yin, Yin Zhang, Amit Chakraborty
CORR
2010
Springer
166views Education» more  CORR 2010»
13 years 6 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
IPMI
2009
Springer
14 years 7 months ago
Neural Tractography Using An Unscented Kalman Filter
We describe a technique to simultaneously estimate a local neural fiber model and trace out its path. Existing techniques estimate the local fiber orientation at each voxel indepen...
James G. Malcolm, Martha Elizabeth Shenton, Yogesh...