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research article

Sparsity Averaging for Compressive Imaging

Carrillo, Rafael  
•
McEwen, Jason  
•
Van De Ville, Dimitri  
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2013
IEEE Signal Processing Letters

We discuss a novel sparsity prior for compressive imaging in the context of the theory of compressed sensing with coherent redundant dictionaries, based on the observation that natural images exhibit strong average sparsity over multiple coherent frames. We test our prior and the associated algorithm, based on an analysis reweighted $\ell_1$ formulation, through extensive numerical simulations on natural images for spread spectrum and random Gaussian acquisition schemes. Our results show that average sparsity outperforms state-of-the-art priors that promote sparsity in a single orthonormal basis or redundant frame, or that promote gradient sparsity. Code and test data are available at https://github.com/basp-group/sopt.

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Type
research article
DOI
10.1109/LSP.2013.2259813
Web of Science ID

WOS:000318762000002

Author(s)
Carrillo, Rafael  
McEwen, Jason  
Van De Ville, Dimitri  
Thiran, Jean-Philippe  
Wiaux, Yves  
Date Issued

2013

Publisher

Institute of Electrical and Electronics Engineers

Published in
IEEE Signal Processing Letters
Volume

20

Issue

6

Start page

591

End page

594

Subjects

CIBM-SPC

•

LTS5

•

LTS2

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTS5  
LTS2  
Available on Infoscience
July 31, 2012
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/84336
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