Referenced Items are not available for this document.
We propose new deterministic low-storage constructions of compressive sampling matrices based on classical finite-geometry generalized polygons. For the noiseless measurements case, we develop a novel exact-recovery algorithm for strictly sparse signals that utilizes the geometry properties of generalized polygons and exhibits complexity linear in the sparsity value. In the presence of measurement noise, recovery of the generalized-polygon sampled signals can be carried out effectively using a belief propagation algorithm.
Published in:
Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on
Date of Conference: 7-10 Nov. 2010
- Page(s):
- 359 - 363
- ISSN :
- 1058-6393
- Print ISBN:
- 978-1-4244-9722-5
- INSPEC Accession Number:
- 11972574
- Conference Location :
- Pacific Grove, CA
- Digital Object Identifier :
- 10.1109/ACSSC.2010.5757535
- Product Type:
- Conference Publications
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