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首页> 外文期刊>Electronics and Communications in Japan. Part 3, Fundamental Electronic Science >Optimal Noise Removal Using an Adaptive Wiener Filter Based on a Locally Stationary Gaussian Mixture Distribution Model for Images
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Optimal Noise Removal Using an Adaptive Wiener Filter Based on a Locally Stationary Gaussian Mixture Distribution Model for Images

机译：基于局部平稳高斯混合分布模型的自适应维纳滤波器最优噪声去除

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摘要

This paper proposes an adaptive Wiener filter (AWF) based on the Gaussian mixture distribution model (GMM) as a realization of an optimum restoration filter. The proposed method is a kind of simple adaptive WF that classifies image blocks according to their local statistical properties and selects a matched WF from a class of a priori deduced WFs. In this method, the optimum filter is realized based on the fact that the minimum mean square error filter is reduced to a WF when the image and noise signals are both Gaussian. In addition, the probability distribution function of the signals in each class can be transformed to Gaussian by using the GMM as a statistical model for the image. In order to improve the accuracy of variance estimation and to reduce the computational complexity in deducing the WFs, a universal mixture model obtained from various kinds of training images is used as the underlying mixture model. In this paper, in restoring images corrupted with white noise, the method of estimating the covariance matrices in locally stationary processes and the method of designing the mixture model are studied, and the adaptive WF is designed. Finally, simulation results show the efficiency of the proposed method compared with conventional methods.
机译：本文提出了一种基于高斯混合分布模型（GMM）的自适应维纳滤波器（AWF）作为最优恢复滤波器的实现。所提出的方法是一种简单的自适应WF，其根据图像块的局部统计特性对图像块进行分类，并从一类先验推导的WF中选择匹配的WF。在该方法中，基于以下事实来实现最佳滤波器：当图像和噪声信号均为高斯信号时，最小均方误差滤波器减小为WF。另外，通过使用GMM作为图像的统计模型，可以将每个类别中信号的概率分布函数转换为高斯函数。为了提高方差估计的准确性并减少推导WF的计算复杂性，将从各种训练图像中获得的通用混合模型用作基础混合模型。本文在恢复白噪声破坏的图像时，研究了局部平稳过程中协方差矩阵的估计方法和混合模型的设计方法，并设计了自适应WF。最后，仿真结果表明了该方法与传统方法相比的有效性。

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