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    Improved image recovery from compressed data contaminated with impulsive noise

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    Authors
    Pham, DucSon
    Venkatesh, Svetha
    Date
    2011
    Type
    Journal Article
    
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    Citation
    Pham, 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.
    Source Title
    IEEE Transactions on Image Processing
    DOI
    10.1109/TIP.2011.2162418
    ISSN
    10577149
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/30022
    Collection
    • Curtin Research Publications
    Abstract

    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.

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