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Adaptive Structured Block Sparsity Via Dyadic Partitioning

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Date
2011
Link to item file
http://hal.archives-ouvertes.fr/hal-00597772/fr/
Dewey
Traitement du signal
Sujet
dyadic partition; Stein risk; block-sparsity
Conference name
EUSIPCO 2011
Conference date
08-2011
Conference city
Barcelone
Conference country
Espagne
URI
https://basepub.dauphine.fr/handle/123456789/6507
Collections
  • CEREMADE : Publications
Metadata
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Author
Chesneau, Christophe
Fadili, Jalal
Peyré, Gabriel
Type
Communication / Conférence
Item number of pages
5
Abstract (EN)
This paper proposes a novel method to adapt the block-sparsity structure to the observed noisy data. Towards this goal, the Stein risk estimator framework is exploited, and the block-sparsity is dyadically organized in a tree. The adaptation of the sparsity structure is obtained by finding the best recursive dyadic partition, whose terminal nodes (leaves) are the blocks, that minimizes a data-driven estimator of the risk. Our main contributions are (i) analytical expression of the risk; (ii) a novel estimator of the risk; (iii) a fast algorithm that yields the best partition. Numerical results on wavelet-domain denoising of synthetic and natural images illustrate the improvement brought by our adaptive approach.

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