Recovery of correlated sparse signals from under-sampled measurements

Zhaofu Chen; Rafael Molina; Aggelos K. Katsaggelos

Recovery of correlated sparse signals from under-sampled measurements

Zhaofu Chen, Rafael Molina, Aggelos K. Katsaggelos

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
Publisher	European Signal Processing Conference, EUSIPCO
Pages	451-455
Number of pages	5
ISBN (Electronic)	9780992862619
State	Published - Nov 10 2014
Event	22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal Duration: Sep 1 2014 → Sep 5 2014

Publication series

Name	European Signal Processing Conference
ISSN (Print)	2219-5491

Other

Other	22nd European Signal Processing Conference, EUSIPCO 2014
Country/Territory	Portugal
City	Lisbon
Period	9/1/14 → 9/5/14

Keywords

Sparse signal recovery
convex relaxation method
greedy algorithm
multiple measurement

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

Cite this

@inproceedings{a3c52512bec24c1aa4906c4aa457f6d2,

title = "Recovery of correlated sparse signals from under-sampled measurements",

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.",

keywords = "Sparse signal recovery, convex relaxation method, greedy algorithm, multiple measurement",

author = "Zhaofu Chen and Rafael Molina and Katsaggelos, {Aggelos K.}",

note = "Publisher Copyright: {\textcopyright} 2014 EURASIP.; 22nd European Signal Processing Conference, EUSIPCO 2014 ; Conference date: 01-09-2014 Through 05-09-2014",

year = "2014",

month = nov,

day = "10",

language = "English (US)",

series = "European Signal Processing Conference",

publisher = "European Signal Processing Conference, EUSIPCO",

pages = "451--455",

booktitle = "2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014",

}

Chen, Z, Molina, R & Katsaggelos, AK 2014, Recovery of correlated sparse signals from under-sampled measurements. in 2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014., 6952109, European Signal Processing Conference, European Signal Processing Conference, EUSIPCO, pp. 451-455, 22nd European Signal Processing Conference, EUSIPCO 2014, Lisbon, Portugal, 9/1/14.

Recovery of correlated sparse signals from under-sampled measurements. / Chen, Zhaofu; Molina, Rafael; Katsaggelos, Aggelos K.
2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014. European Signal Processing Conference, EUSIPCO, 2014. p. 451-455 6952109 (European Signal Processing Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Recovery of correlated sparse signals from under-sampled measurements

AU - Chen, Zhaofu

AU - Molina, Rafael

AU - Katsaggelos, Aggelos K.

PY - 2014/11/10

Y1 - 2014/11/10

N2 - 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.

AB - 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.

KW - Sparse signal recovery

KW - convex relaxation method

KW - greedy algorithm

KW - multiple measurement

UR - http://www.scopus.com/inward/record.url?scp=84911897129&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84911897129&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84911897129

T3 - European Signal Processing Conference

SP - 451

EP - 455

BT - 2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014

PB - European Signal Processing Conference, EUSIPCO

T2 - 22nd European Signal Processing Conference, EUSIPCO 2014

Y2 - 1 September 2014 through 5 September 2014

ER -

Recovery of correlated sparse signals from under-sampled measurements

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this