摘要
当图像中同时存在高斯噪声和椒盐噪声时,单一的均值滤波或中值滤波很难达到最佳滤波效果。分析了噪声特点和各种滤波方法的优势,提出了一种基于神经网络的图像混合滤波及融合算法:首先建立概率神经网络,检测椒盐噪声和高斯噪声点,并分别利用中值滤波和均值滤波去除噪声点,然后建立径向基函数神经网络,利用训练好的径向基函数神经网络融合2种不同滤波的图像,输出理想的融合图像。Matlab仿真实验结果表明,该算法有效去除混合噪声的同时,能很好地保护图像的边缘与细节,是一种有效的方法。
When Gaussian noise and salt and pepper noise both exist in image, single mean filter or median filter turn out to be dissatisfactory. The characteristics of noises and dominance of filter algorithms were analyzed. A hybrid filter and fusion algorithm based on neural network was proposed. Firstly, probabilistic neural network was built to detect the salt and pepper noise and Gaussian noise and remove them respectively by median filter and mean filter algorithm. Then trained radial basis function neural network was built to fuse the two kinds of different filtering image. The ideal fusion image was output finally. The results by Maflab simulation experiments showed that the proposed algorithm can effectively remove mixed noise and preserve image edges and details very well. It is an effective method of image denoising.
出处
《包装工程》
CAS
CSCD
北大核心
2013年第9期89-94,共6页
Packaging Engineering
关键词
概率神经网络
径向基函数神经网络
中值滤波
均值滤波
混合滤波
融合算法
probabilistic neural network （PNN）
radial basis function neural network （RBFNN）
median filter
mean filter
hybrid filter algorithm
fusion algorithm
