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Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling
Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling
Author
Sermwuthisarn P, Auethavekiat S, Gansawat D, Patanavijit V
Chulalongkorn University; National Science & Technology Development Agency - Thailand; National Electronics & Computer Technology Center (NECTEC)
Type
Article
Source Title
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
ISSN
1687-6180
Year
2012
Page
-
Open Access
gold
Publisher
SPRINGEROPEN
DOI
10.1186/1687-6180-2012-34
Format
PDF
Abstract
The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.
National Telecommunications Commission Fund [PHD/006/2551]; Telecommunications Research Industrial and Development Institute (TRIDI)
License
CC-BY
Rights
Authors
Publication Source
WOS
Document
Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling