Large scale 2D spectral compressed sensing in continuous domain

Jian-Feng Cai; Weiyu Xu; Yang Yang

doi:10.1109/icassp.2017.7953289

Large scale 2D spectral compressed sensing in continuous domain

Jian-Feng Cai, Weiyu Xu, Yang Yang

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

8 Scopus citations

Abstract

We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500 × 500, whereas traditional approaches only handle signals of size around 20 × 20.

Original language	English (US)
Title of host publication	2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	5905-5909
Number of pages	5
ISBN (Print)	9781509041176
DOIs	https://doi.org/10.1109/icassp.2017.7953289
State	Published - Jun 20 2017
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OCRF-2014-CRG-3
Acknowledgements: JFC is supported in part by Grant 16300616 of Hong Kong Research Grants Council. Weiyu Xu is supported by the Simons Foundation 318608 , KAUST OCRF-2014-CRG-3, NSF DMS-1418737 and NIH lROlEB020665-01
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1109/icassp.2017.7953289

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title = "Large scale 2D spectral compressed sensing in continuous domain",

abstract = "We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500 × 500, whereas traditional approaches only handle signals of size around 20 × 20.",

author = "Jian-Feng Cai and Weiyu Xu and Yang Yang",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OCRF-2014-CRG-3 Acknowledgements: JFC is supported in part by Grant 16300616 of Hong Kong Research Grants Council. Weiyu Xu is supported by the Simons Foundation 318608 , KAUST OCRF-2014-CRG-3, NSF DMS-1418737 and NIH lROlEB020665-01 This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2017",

month = jun,

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doi = "10.1109/icassp.2017.7953289",

language = "English (US)",

isbn = "9781509041176",

pages = "5905--5909",

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T1 - Large scale 2D spectral compressed sensing in continuous domain

AU - Cai, Jian-Feng

AU - Xu, Weiyu

AU - Yang, Yang

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OCRF-2014-CRG-3 Acknowledgements: JFC is supported in part by Grant 16300616 of Hong Kong Research Grants Council. Weiyu Xu is supported by the Simons Foundation 318608 , KAUST OCRF-2014-CRG-3, NSF DMS-1418737 and NIH lROlEB020665-01 This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2017/6/20

Y1 - 2017/6/20

N2 - We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500 × 500, whereas traditional approaches only handle signals of size around 20 × 20.

AB - We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500 × 500, whereas traditional approaches only handle signals of size around 20 × 20.

UR - http://hdl.handle.net/10754/625802

UR - http://ieeexplore.ieee.org/document/7953289/

UR - http://www.scopus.com/inward/record.url?scp=85023759895&partnerID=8YFLogxK

U2 - 10.1109/icassp.2017.7953289

DO - 10.1109/icassp.2017.7953289

M3 - Conference contribution

SN - 9781509041176

SP - 5905

EP - 5909

BT - 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Large scale 2D spectral compressed sensing in continuous domain

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