15 search results - page 2 / 3 » Using reed-muller sequences as deterministic compressed sens... |
ICASSP
2011
IEEE
12 years 9 months ago
2011
IEEE
Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. In this paper, a K × N measurement matrix for compressed sensing ...
JC
2007
13 years 5 months ago
2007
Compressed sensing is a new area of signal processing. Its goal is to minimize the number of samples that need to be taken from a signal for faithful reconstruction. The performan...
ICIP
2009
IEEE
13 years 3 months ago
2009
IEEE
Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., ...
ICASSP
2009
IEEE
13 years 12 months ago
2009
IEEE
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...
ICIP
2009
IEEE
14 years 6 months ago
2009
IEEE
In this work, we propose algorithms to recursively and causally reconstruct a sequence of natural images from a reduced number of linear projection measurements taken in a domain ...