IEEE Trans Image Process - Improved image recovery from compressed data contaminated with impulsive noise.

Tópicos

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Resumo

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 l(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.

Resumo Limpo

compress sens cs new inform sampl theori acquir spars compress data much fewer measur otherwis requir nyquistshannon counterpart particular import imag applic magnet reson imag astronomi howev exist cs formul use l norm residu particular effici nois impuls lead increas upper bound recoveri error address problem consid robust formul cs suppress outlier residu propos iter algorithm solv robust cs problem exploit power exist cs solver also show upper bound recoveri error case nongaussian nois reduc demonstr efficaci method numer studi

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