Recovery of correlated sparse signals from under-sampled measurements

Zhaofu Chen, Rafael Molina, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper we consider the problem of recovering temporally smooth or correlated sparse signals from a set of undersampled measurements. We propose two algorithmic solutions that exploit the signal temporal properties to improve the reconstruction accuracy. The effectiveness of the proposed algorithms is corroborated with experimental results.

Original languageEnglish (US)
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages451-455
Number of pages5
ISBN (Electronic)9780992862619
StatePublished - Nov 10 2014
Event22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal
Duration: Sep 1 2014Sep 5 2014

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Other

Other22nd European Signal Processing Conference, EUSIPCO 2014
CountryPortugal
CityLisbon
Period9/1/149/5/14

Keywords

  • Sparse signal recovery
  • convex relaxation method
  • greedy algorithm
  • multiple measurement

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Recovery of correlated sparse signals from under-sampled measurements'. Together they form a unique fingerprint.

Cite this