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DR image denoising based on Laplace-Impact mixture model / 中国医疗器械杂志
Chinese Journal of Medical Instrumentation ; (6): 247-250, 2009.
Article Dans Chinois | WPRIM | ID: wpr-329331
ABSTRACT
A novel DR image denoising algorithm based on Laplace-Impact mixture model in dual-tree complex wavelet domain is proposed in this paper. It uses local variance to build probability density function of Laplace-Impact model fitted to the distribution of high-frequency subband coefficients well. Within Laplace-Impact framework, this paper describes a novel method for image denoising based on designing minimum mean squared error (MMSE) estimators, which relies on strong correlation between amplitudes of nearby coefficients. The experimental results show that the algorithm proposed in this paper outperforms several state-of-art denoising methods such as Bayes least squared Gaussian scale mixture and Laplace prior.
Sujets)
Texte intégral: Disponible Indice: WPRIM (Pacifique occidental) Sujet Principal: Algorithmes / Amélioration d'image radiographique / Modèles statistiques / Méthodes Type d'étude: Facteurs de risque langue: Chinois Texte intégral: Chinese Journal of Medical Instrumentation Année: 2009 Type: Article

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Texte intégral: Disponible Indice: WPRIM (Pacifique occidental) Sujet Principal: Algorithmes / Amélioration d'image radiographique / Modèles statistiques / Méthodes Type d'étude: Facteurs de risque langue: Chinois Texte intégral: Chinese Journal of Medical Instrumentation Année: 2009 Type: Article