Recovery of Cosparse Signals with Greedy Analysis Pursuit in the Presence of Noise

Sangnam Nam 1 Mike Davies 2 Michael Elad 3 Rémi Gribonval 1
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The sparse synthesis signal model has enjoyed much success and popularity in the recent decade. Much progress ranging from clear theoretical foundations to appealing applications has been made in this field. Alongside the synthesis approach, an analysis counterpart has been used over the years. Despite the similarity, markedly different nature of the two approaches has been observed. In a recent work, the analysis model was formally formulated and the nature of the model was discussed extensively. Furthermore, a new greedy algorithm (GAP) for recovering the signals satisfying the model was proposed and its effectiveness was demonstrated. While the understanding of the analysis model and the new algorithm has been broadened, the stability and the robustness against noise of the model and the algorithm have been mostly left out. In this work, we adapt and propose a new GAP algorithm in order to deal with the presence of noise. Empirical evidence for the algorithm is also provided.
Type de document :
Communication dans un congrès
CAMSAP - 4th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - 2011, Dec 2011, San Juan, Puerto Rico. 2011
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https://hal.inria.fr/hal-00691162
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Dernière modification le : vendredi 13 janvier 2017 - 14:19:10
Document(s) archivé(s) le : lundi 26 novembre 2012 - 15:45:39

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Sangnam Nam, Mike Davies, Michael Elad, Rémi Gribonval. Recovery of Cosparse Signals with Greedy Analysis Pursuit in the Presence of Noise. CAMSAP - 4th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - 2011, Dec 2011, San Juan, Puerto Rico. 2011. <hal-00691162>

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