Structure dependent sampling in compressed sensing: theoretical guarantees for tight frames

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Many of the applications of compressed sensing have been based on variable density sampling, where certain sections of the sampling coefficients are sampled more densely. Furthermore, it has been observed that these sampling schemes are dependent not only on sparsity but also on the sparsity structure of the underlying signal. This paper extends the result of Adcock, Hansen, Poon and Roman (arXiv:1302.0561, 2013) [2] to the case where the sparsifying system forms a tight frame. By dividing the sampling coefficients into levels, our main result will describe how the amount of subsampling in each level is determined by the local coherences between the sampling and sparsifying operators and the localized level sparsities – the sparsity in each level under the sparsifying operator.
Original languageEnglish
Pages (from-to)402-451
Number of pages50
JournalApplied and Computational Harmonic Analysis
Volume42
Issue number3
Early online date18 Sep 2015
DOIs
Publication statusPublished - 31 May 2017

Cite this

Structure dependent sampling in compressed sensing: theoretical guarantees for tight frames. / Poon, Clarice.

In: Applied and Computational Harmonic Analysis, Vol. 42, No. 3, 31.05.2017, p. 402-451.

Research output: Contribution to journalArticle

@article{37ea606936c44a869e4a138dcc1d0b66,
title = "Structure dependent sampling in compressed sensing: theoretical guarantees for tight frames",
abstract = "Many of the applications of compressed sensing have been based on variable density sampling, where certain sections of the sampling coefficients are sampled more densely. Furthermore, it has been observed that these sampling schemes are dependent not only on sparsity but also on the sparsity structure of the underlying signal. This paper extends the result of Adcock, Hansen, Poon and Roman (arXiv:1302.0561, 2013) [2] to the case where the sparsifying system forms a tight frame. By dividing the sampling coefficients into levels, our main result will describe how the amount of subsampling in each level is determined by the local coherences between the sampling and sparsifying operators and the localized level sparsities – the sparsity in each level under the sparsifying operator.",
author = "Clarice Poon",
year = "2017",
month = "5",
day = "31",
doi = "10.1016/j.acha.2015.09.003",
language = "English",
volume = "42",
pages = "402--451",
journal = "Applied and Computational Harmonic Analysis",
issn = "1063-5203",
publisher = "Elsevier Masson",
number = "3",

}

TY - JOUR

T1 - Structure dependent sampling in compressed sensing: theoretical guarantees for tight frames

AU - Poon, Clarice

PY - 2017/5/31

Y1 - 2017/5/31

N2 - Many of the applications of compressed sensing have been based on variable density sampling, where certain sections of the sampling coefficients are sampled more densely. Furthermore, it has been observed that these sampling schemes are dependent not only on sparsity but also on the sparsity structure of the underlying signal. This paper extends the result of Adcock, Hansen, Poon and Roman (arXiv:1302.0561, 2013) [2] to the case where the sparsifying system forms a tight frame. By dividing the sampling coefficients into levels, our main result will describe how the amount of subsampling in each level is determined by the local coherences between the sampling and sparsifying operators and the localized level sparsities – the sparsity in each level under the sparsifying operator.

AB - Many of the applications of compressed sensing have been based on variable density sampling, where certain sections of the sampling coefficients are sampled more densely. Furthermore, it has been observed that these sampling schemes are dependent not only on sparsity but also on the sparsity structure of the underlying signal. This paper extends the result of Adcock, Hansen, Poon and Roman (arXiv:1302.0561, 2013) [2] to the case where the sparsifying system forms a tight frame. By dividing the sampling coefficients into levels, our main result will describe how the amount of subsampling in each level is determined by the local coherences between the sampling and sparsifying operators and the localized level sparsities – the sparsity in each level under the sparsifying operator.

U2 - 10.1016/j.acha.2015.09.003

DO - 10.1016/j.acha.2015.09.003

M3 - Article

VL - 42

SP - 402

EP - 451

JO - Applied and Computational Harmonic Analysis

JF - Applied and Computational Harmonic Analysis

SN - 1063-5203

IS - 3

ER -