摘要
提出一种新的基于几何特征的模糊神经网络去噪方法.首先用裂缝8个种类的几何特征向量对噪声点的估计作为输入,建立起一个模糊神经网络系统(FNN),并对大量的噪声图像进行训练,然后将训练好的FNN用于判断图像点是否为噪声点,如果是噪声点,则用传统的中值滤波进行去噪,否则不作任何操作,保留原图像作为重构输出图像.实验结果表明,该方法既消除了噪声,又很好的保留了图像的细节,效果令人满意.
This paper proposes an new denosing method based on fuzzy neural network of geometrical features using crack its own characteristics.Its basic idea is to firstly measure 8kinds of geometric feature vector for noise estimation as input to establish a fuzzy neural network system(FNN)to train many images.Then it use the trained FNN to judge whether the image points for the noise point,if the result is noise,it is denoised using the classical median filter,Otherwise,retained the original image as the reconstruction of the output image without any operation.The experimental results show that this method can eliminate the noise,and keep agood image details.Finally,the effect is satisfied.
出处
《微电子学与计算机》
CSCD
北大核心
2014年第9期39-41,47,共4页
Microelectronics & Computer
基金
国家自然科学基金(51208434)
中央高校基本科研业务费专项资金(SWJTU12CX070)
关键词
裂缝检测
几何特征
模糊神经网络
中值滤波
crack detection
geometrical features
fuzzy neural network
median filter
