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Improved image recovery from compressed data contaminated with impulsive noise

Pham, Duc-Son and Venkatesh, Svetha 2012, Improved image recovery from compressed data contaminated with impulsive noise, IEEE transactions on image processing, vol. 21, no. 1, pp. 397-405.

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Title Improved image recovery from compressed data contaminated with impulsive noise
Author(s) Pham, Duc-Son
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name IEEE transactions on image processing
Volume number 21
Issue number 1
Start page 397
End page 405
Total pages 9
Publisher IEEE
Place of publication Piscataway, N. J
Publication date 2012-01
ISSN 1057-7149
1941-0042
Keyword(s) compressed sensing (CS)
image compression
impulsive noise
inverse problems
robust recovery
robust statistics
Summary Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in astronomy. However, in the existing CS formulation, the use of the â„“ 2 norm on the residuals is not particularly efficient when the noise is impulsive. This could lead to an increase in the upper bound of the recovery error. To address this problem, we consider a robust formulation for CS to suppress outliers in the residuals. We propose an iterative algorithm for solving the robust CS problem that exploits the power of existing CS solvers. We also show that the upper bound on the recovery error in the case of non-Gaussian noise is reduced and then demonstrate the efficacy of the method through numerical studies.
Language eng
Field of Research 080106 Image Processing
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044224

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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