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Bayesian Non Local Means-Based Speckle filtering

Pierrick Coupé 1 Pierre Hellier 1 Charles Kervrann 2 Christian Barillot 1 
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In ultrasound (US) imaging, denoising is intended to improve quantitative image analysis techniques. In this paper, a new version of the Non Local (NL) Means filter adapted for US images is proposed. Originally developed for Gaussian noise removal, a Bayesian framework is used to adapt the NL means filter for speckle noise. Experiments were carried out on synthetic data sets with different speckle simulations. Results show that our NL means-based speckle filter outperforms the classical implementation of the NL means filter, as well as two other speckle adapted denoising methods (SRAD and SBF filters).
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Submitted on : Friday, May 30, 2008 - 10:41:23 AM
Last modification on : Saturday, June 25, 2022 - 7:45:48 PM
Long-term archiving on: : Friday, September 28, 2012 - 3:10:33 PM


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  • HAL Id : inria-00283477, version 1


Pierrick Coupé, Pierre Hellier, Charles Kervrann, Christian Barillot. Bayesian Non Local Means-Based Speckle filtering. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, May 2008, Paris, France. ⟨inria-00283477⟩



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