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Improved image recovery from compressed data contaminated with impulsive noise
journal contribution
posted on 2012-01-01, 00:00 authored by D S Pham, Svetha VenkateshSvetha VenkateshCompressed 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.
History
Journal
IEEE transactions on image processingVolume
21Issue
1Pagination
397 - 405Publisher
IEEELocation
Piscataway, N. JISSN
1057-7149eISSN
1941-0042Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2011, IEEEUsage metrics
Keywords
compressed sensing (CS)image compressionimpulsive noiseinverse problemsrobust recoveryrobust statisticsScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringINTERIOR-POINT METHODSIGNAL RECONSTRUCTIONGRADIENTArtificial Intelligence and Image Processing
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