Cosparse Analysis Modeling

Abstract : In the past decade there has been a great interest in a synthesis-based model for signals, based on sparse and redundant representations. This work considers an alternative analysis-based model, where an analysis operator multiplies the signal, leading to a cosparse outcome. We consider this analysis model, in the context of a generic missing data problem. Our work proposes a uniqueness result for the solution of this problem, based on properties of the analysis operator and the measurement matrix. A new greedy algorithm for solving the missing data problem is proposed along with theoretical study of the success of the algorithm and experimental results.
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Communication dans un congrès
Workshop on Signal Processing with Adaptive Sparse Structured Representations, Jun 2011, Edinburgh, United Kingdom. 2011
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Contributeur : Sangnam Nam <>
Soumis le : vendredi 16 septembre 2011 - 11:17:00
Dernière modification le : jeudi 11 janvier 2018 - 06:20:09
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  • HAL Id : inria-00587943, version 1

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Sangnam Nam, Mike E. Davies, Michael Elad, Rémi Gribonval. Cosparse Analysis Modeling. Workshop on Signal Processing with Adaptive Sparse Structured Representations, Jun 2011, Edinburgh, United Kingdom. 2011. 〈inria-00587943〉

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