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

被引：0

作者：

Nacereddine, Nafaa ^{[1
]}

Goumeidane, Aicha Baya ^{[1
]}

机构：

[1] Res Ctr Ind Technol, POB 64, Algiers 16014, Algeria

来源：

2019 IEEE 15TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP 2019) | 2019年

关键词：

Asymmetric generalized Gaussian distribution; Parameter estimation; Maximum likelihood; Moments; Entropy;

D O I：

10.1109/iccp48234.2019.8959693

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we address the problem of estimating the parameters of Asymmetric Generalized Gaussian Distribution (AGGD) using three estimation mehods, namely, Maximum Likelihood Estimation (NILE), Moment Matching Estimation (MIME) and Entropy Matching Estimaion (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.

引用

页码：343 / 350

页数：8

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