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dc.contributor.authorPham, DucSon
dc.contributor.authorVenkatesh, Svetha
dc.identifier.citationPham, Duc-Son and Venkatesh, S. 2011. Improved image recovery from compressed data contaminated with impulsive noise. IEEE Transactions on Image Processing. 21 (1): pp. 397-404.

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 /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.

dc.publisherIEEE Signal Processing Society
dc.subjectinverse problems
dc.subjectimpulsive noise
dc.subjectrobust statistics
dc.subjectCompressed sensing (CS)
dc.subjectimage compression
dc.subjectrobust recovery
dc.titleImproved image recovery from compressed data contaminated with impulsive noise
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Image Processing
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available

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