Signal recovery from incomplete measurements in the presence of outliers | Zendy

B. Popilka | Zendy; Simon Setzer | Zendy; Gabriele Steidl | Zendy

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Signal recovery from incomplete measurements in the presence of outliers

Author(s) -

B. Popilka,

Simon Setzer,

Gabriele Steidl

Publication year - 2007

Publication title -

inverse problems and imaging

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.755

H-Index - 40

eISSN - 1930-8345

pISSN - 1930-8337

DOI - 10.3934/ipi.2007.1.661

Subject(s) - outlier , regularization (linguistics) , signal (programming language) , computer science , algorithm , gaussian , additive white gaussian noise , image (mathematics) , gaussian noise , image restoration , signal recovery , compressed sensing , artificial intelligence , mathematics , white noise , pattern recognition (psychology) , image processing , physics , telecommunications , programming language , quantum mechanics

We study the restoration of a sparse signal or an image with a sparse gradient from a relatively small number of linear measurements which are additionally corrupted by a small amount of white Gaussian noise and outliers. We minimize $\l_1-\l_1$ and $\l_1-TV$ regularization functionals using various algorithms and present numerical results for different measurement matrices as well as different sparsity levels of the initial signal/image and of the outlier vector.

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