摘要
在噪声污染的磁共振图像中,运用MCA和Context模型结合的算法进行处理,以达到尽可能完全滤除噪声的目的。该方法的主要思想是用MCA方法将图像分离成两个部分,分别采用二进小波和Context模型对这两部分进行处理。仿真实验结果表明,本方法是有效可用的,提高了图像的视觉效果,具有更高的PSNR值。
In the MRI image of noise pollution, the MCA and the Context model union algorithm is used as the method of MRI image denosing, in order to achieve the aim of completely filtering out the noise as much as possible. The main idea of this method is that the MCA method is used to separate the image into two parts, using the dyadic wavelet and the context model to deal with these two parts respectively. The simulation results show that this meth- od is effective and available with better visual effect and a higher PSNR value.
出处
《电视技术》
北大核心
2012年第23期12-14,共3页
Video Engineering
关键词
磁共振图像
形态成分分析
context模型
阈值滤波
图像去噪
magnetic resonance image
morphological component analysis
context model
threshold filtering
image denosing
