Journal
COMPTES RENDUS MATHEMATIQUE
Volume 346, Issue 9-10, Pages 589-592Publisher
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.crma.2008.03.014
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Funding
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0965028] Funding Source: National Science Foundation
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It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements, possibly contaminated with noise. This technique known as compressed sensing or compressive sampling relies on properties of the sensing matrix such as the restricted isometry property. In this Note, we establish new results about the accuracy of the reconstruction from undersampled measurements which improve on earlier estimates, and have the advantage of being more elegant.
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