Iterative Recovery of Dense Signals from Incomplete Measurements

Norbert Goertz; Chunli Guo; Alexander Jung; Mike E. Davies; Gerhard Doblinger

doi:10.1109/LSP.2014.2323973

Iterative Recovery of Dense Signals from Incomplete Measurements

Norbert Goertz^*, Chunli Guo, Alexander Jung, Mike E. Davies, Gerhard Doblinger

^*Corresponding author for this work

School of Engineering

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

Within the framework of compressed sensing, we consider dense signals, which contain both discrete as well as continuous-amplitude components. We demonstrate by a comprehensive numerical study-to the best of our knowledge the first of its kind in the literature-that dense signals can be recovered from noisy, incomplete linear measurements by simple iterative algorithms that are inspired by or are implementations of approximate message passing. Those iterative algorithms are shown to significantly outperform all other algorithms presented so far, when they use a novel noise-adaptive thresholding function that is proposed in this contribution.

Original language	English
Pages (from-to)	1059-1063
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	21
Issue number	9
DOIs	https://doi.org/10.1109/LSP.2014.2323973
Publication status	Published - Sept 2014

Keywords / Materials (for Non-textual outputs)

Approximate message passing
compressed sensing
dense signals
iterative recovery

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10.1109/LSP.2014.2323973

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title = "Iterative Recovery of Dense Signals from Incomplete Measurements",

abstract = "Within the framework of compressed sensing, we consider dense signals, which contain both discrete as well as continuous-amplitude components. We demonstrate by a comprehensive numerical study-to the best of our knowledge the first of its kind in the literature-that dense signals can be recovered from noisy, incomplete linear measurements by simple iterative algorithms that are inspired by or are implementations of approximate message passing. Those iterative algorithms are shown to significantly outperform all other algorithms presented so far, when they use a novel noise-adaptive thresholding function that is proposed in this contribution.",

keywords = "Approximate message passing, compressed sensing, dense signals, iterative recovery",

author = "Norbert Goertz and Chunli Guo and Alexander Jung and Davies, {Mike E.} and Gerhard Doblinger",

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T1 - Iterative Recovery of Dense Signals from Incomplete Measurements

AU - Goertz, Norbert

AU - Guo, Chunli

AU - Jung, Alexander

AU - Davies, Mike E.

AU - Doblinger, Gerhard

PY - 2014/9

Y1 - 2014/9

N2 - Within the framework of compressed sensing, we consider dense signals, which contain both discrete as well as continuous-amplitude components. We demonstrate by a comprehensive numerical study-to the best of our knowledge the first of its kind in the literature-that dense signals can be recovered from noisy, incomplete linear measurements by simple iterative algorithms that are inspired by or are implementations of approximate message passing. Those iterative algorithms are shown to significantly outperform all other algorithms presented so far, when they use a novel noise-adaptive thresholding function that is proposed in this contribution.

AB - Within the framework of compressed sensing, we consider dense signals, which contain both discrete as well as continuous-amplitude components. We demonstrate by a comprehensive numerical study-to the best of our knowledge the first of its kind in the literature-that dense signals can be recovered from noisy, incomplete linear measurements by simple iterative algorithms that are inspired by or are implementations of approximate message passing. Those iterative algorithms are shown to significantly outperform all other algorithms presented so far, when they use a novel noise-adaptive thresholding function that is proposed in this contribution.

KW - Approximate message passing

KW - compressed sensing

KW - dense signals

KW - iterative recovery

U2 - 10.1109/LSP.2014.2323973

DO - 10.1109/LSP.2014.2323973

M3 - Article

SN - 1070-9908

VL - 21

SP - 1059

EP - 1063

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

IS - 9

ER -

Iterative Recovery of Dense Signals from Incomplete Measurements

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

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