Decision-based fuzzy image restoration for noise reduction based on evidence theory
Decision-based fuzzy image restoration for noise reduction based on evidence theory
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DOI：
10.1016/j.eswa.2011.01.016
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
Tzu-Chao Lin
中科院分区：
文献类型：
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作者：
Tzu-Chao Lin
提出了一种新型的基于判决的模糊平均(DFA)滤波器，它由D-S噪声检测器和双通噪声滤波机构组成。所提出的滤波器能有效地处理脉冲噪声、高斯噪声和脉冲噪声的混合。该方法提取证据主体，利用简单支持函数构造基本信念赋值，避免了Dempster组合规则的反直觉问题。组合信任值是D-S噪声检测器的决策规则。为了实现噪声消除，提出了一种模糊平均方法，其中的权重是使用预定义的模糊集构造的。为了提高最终的滤波性能，采用了简单的二通滤波器。实验结果证实了新的DFA滤波器在抑制脉冲噪声以及混合高斯和脉冲噪声以及改善视觉图像质量方面的有效性。
A novel decision-based fuzzy averaging (DFA) filter consisting of a D–S (Dempster–Shafer) noise detector and a two-pass noise filtering mechanism is presented in this paper. The proposed filter can effectively deal with impulsive noise, and a mix of Gaussian and impulsive noise. Bodies of evidence are extracted, and the basic belief assignment is developed using the simple support function, which avoids the counter-intuitive problem of Dempster’s combination rule. The combination belief value is the decision rule for the D–S noise detector. A fuzzy averaging method, where the weights are constructed using a predefined fuzzy set, is developed to achieve noise cancellation. A simple second-pass filter is employed to improve the final filtering performance. Experimental results confirm the effectiveness of the new DFA filter both in suppressing impulsive noise as well as a mix Gaussian and impulsive noise and in improving perceived image quality.