By Topic

Explicit measurements with almost optimal thresholds for compressed sensing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Parvaresh, F. ; Center for Math. of Inf., California Inst. of Technol., Pasadena, CA ; Hassibi, B.

We consider the deterministic construction of a measurement matrix and a recovery method for signals that are block sparse. A signal that has dimension N = nd, which consists of n blocks of size d, is called (s, d)-block sparse if only s blocks out of n are nonzero. We construct an explicit linear mapping Phi that maps the (s, d) -block sparse signal to a measurement vector of dimension M, where s - d < N (1- (1- M/N)d/d+1) - o(1). We show that if the (s,d)- block sparse signal is chosen uniformly at random then the signal can almost surely be reconstructed from the measurement vector in O(N3) computations.

Published in:

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Date of Conference:

March 31 2008-April 4 2008