By Topic

Compressed Sensing via Dual Frame Based \ell _{1} -Analysis With Weibull Matrices

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
$33 $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)
Xiaoya Zhang ; Department of Mathematics, Zhejiang University, Hangzhou, People's Republic of China ; Song Li

This letter considers the problem of recovering signals via dual frame based ℓ1-analysis model under the assumption that signals are compressible in a general frame. Our main result shows that Weibull random matrices (not only subgaussian matrices) with optimal number of measurements could guarantee accurate recovery of signals with high probability. We derive that result by generalizing a recent Lemma due to Foucart and with the help of the parameterized representation of any dual frame. Our result should be significant for existing and upcoming ℓ1-analysis models for signal recovery.

Published in:

IEEE Signal Processing Letters  (Volume:20 ,  Issue: 3 )