Asymmetric Generalized Gaussian Distribution Parameters Estimation based on Maximum Likelihood, Moments and Entropy

Type : Article de conférence
Auteur(s) :  Nafaa Nacereddine, Aicha Baya Goumeidane
Année :  2019
Domaine : Electronique
Conférence: 15th IEEE International Conference on Intelligent Computer Communication and Processing
Résumé en PDF :  (résumé en pdf)
Fulltext en PDF :  (.pdf)
Mots clés :  Asymmetric generalized Gaussian distribution, Parameter estimation, maximum likelihood, Moments, Entropy

Résumé : 

In this paper, we address the problem of estimating the parameters of Asymmetric Generalized Gaussian Distribution (AGGD) using three estimation methods, namely, Maximum Likelihood Estimation (MLE), Moment Matching Estimation (MME) and Entropy Matching Estimation (EME). For this purpose, these methods are applied on an unimodal histogram fitting of an image corrupted with AGGD noise. Experiments show that the effectiveness of each method comparatively to the other one depends on the variation range of the shape factor.