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Learning Analysis Sparsity Priors

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Date
2011
Link to item file
http://hal.archives-ouvertes.fr/hal-00542016/fr/
Dewey
Traitement du signal
Sujet
Dictionary learning; denoising; analysis prior; total variation
Conference name
9th International Conference on Sampling Theory and Applications (SAMPTA 2011)
Conference date
05-2011
Conference city
Singapour
Conference country
Singapour
URI
https://basepub.dauphine.fr/handle/123456789/5241
Collections
  • CEREMADE : Publications
Metadata
Show full item record
Author
Peyré, Gabriel
Fadili, Jalal
Type
Communication / Conférence
Item number of pages
4
Abstract (EN)
This paper introduces a novel approach to learn a dictionary in a sparsity-promoting analysis-type prior. The dictionary is opti- mized in order to optimally restore a set of exemplars from their degraded noisy versions. Towards this goal, we cast our prob- lem as a bilevel programming problem for which we propose a gradient descent algorithm to reach a stationary point that might be a local minimizer. When the dictionary analysis operator specializes to a convolution, our method turns out to be a way of learning generalized total variation-type prior. Applications to 1-D signal denoising are reported and potential applicability and extensions are discussed.

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