Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors

Esteban Vera, Luis Mancera, S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

16 Citas (Scopus)

Resumen

In this paper, we propose a novel algorithm for image reconstruction from compressive measurements of wavelet coefficients. By incorporating independent Laplace priors on separate wavelet sub-bands, the inhomogeneity of wavelet coefficient distributions and therefore the structural sparsity within images are modeled effectively. We model the problem by adopting a Bayesian formulation, and develop a fast greedy reconstruction algorithm. Experimental results demonstrate that the reconstruction performance of the proposed algorithm is competitive with state-of-the-art methods while outperforming them in terms of running times.

Idioma originalInglés
Título de la publicación alojada2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Páginas229-232
Número de páginas4
DOI
EstadoPublicada - 2009
Publicado de forma externa
Evento2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 - Cardiff, Reino Unido
Duración: 31 ago. 20093 sept. 2009

Serie de la publicación

NombreIEEE Workshop on Statistical Signal Processing Proceedings

Conferencia

Conferencia2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
País/TerritorioReino Unido
CiudadCardiff
Período31/08/093/09/09

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