去除乘性噪声的重加权各向异性全变差模型

王旭东; 冯象初; 霍雷刚

doi:10.3724/SP.J.1004.2012.00444

去除乘性噪声的重加权各向异性全变差模型

doi: 10.3724/SP.J.1004.2012.00444

1.
西安电子科技大学理学院应用数学系西安 710071

详细信息

通讯作者:
王旭东, 西安电子科技大学理学院应用数学系博士研究生. 主要研究方向为图像处理的偏微分方程方法.E-mail: xudwang@mail.xidian.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2011-07-04
- 修回日期: 2011-10-08
- 刊出日期: 2012-03-20

Iteratively Reweighted Anisotropic-TV Based Multiplicative Noise Removal Model

1.
Department of Applied Mathematics, School of Science, Xidian University, Xi'an 710071

摘要

摘要: 恢复含乘性噪声的图像是当前图像处理的重要研究课题. 本文提出基于迭代重加权的各向异性全变差(Total variation, TV)模型. 新模型中, 假定乘性噪声服从Gamma分布. 正则项采用加权的各向异性全变差, 其中, 自适应权函数由期望最大(Expectation maximization, EM)算法得到. 新模型在有效去噪的同时, 较好地保留了图像的边缘和细节信息, 同时能够有效地抑制"阶梯效应". 数值实验验证了新模型的效果.
- 图像去噪 /
- 乘性噪声 /
- 期望最大算法 /
- 全变差 /
- 迭代重加权
Abstract: Multiplicative noise removal is an important research topic on image processing. This paper proposes an iteratively reweighted anisotropic-total variation (TV) based model under the assumption that the multiplicative noise follows a Gamma distribution. The regularization term is the weighted anisotropic-TV regularizer. The weighting function incorporated in the regularization term is derived from the expectation maximization (EM) algorithm. The merits of this model are the preservation of geometrical structures of edges and the restraint of "staircase effect" while removing the noise. Numerical experimental results demonstrate the better performance of the proposed model.
- Image denoising /
- multiplicative noise /
- expectation maximization (EM) algorithms /
- total variation (TV) /
- iteratively reweighted method

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