去除乘性噪音的主成分分析算法

Principal Component Analysis Method for Muitiplicative Noise Removal

雷达成像系统的进一步应用依赖于对图像中噪音的有效抑制.在目前现有消除噪音方法的基础上,基于图像的局部相似性,结合主成分分析法,提出一种新的有效去除乘性噪音的滤波算法.乘性噪音经对数变换后可转化为加性噪音处理.分析了对数域中噪音的类型.首先在图像的对数域,通过非局部方法选取局部相似块作为训练样本,利用主成分分析法提取出信号的主要特征.然后基于统计理论中最小均方误差估计法给出了一种适用于图像信息的阈值原则.最后分析了变换过程引起的偏差,由对数域的偏估计得到滤波图像.数值实验验证了新算法的有效性.对比于目前提出的变分方法,新算法处理后的图像有更高的信噪比和更好的视觉效果,且具有一定的实用性.

The further application of Radar image system relies on the quality of denoising from images.By analyzing the existing denoising algorithms,a new algorithm was presented using principal component analysis for removing multiplicative noise,based on local similarity of images.Multiplicative noise by logarithmic transformation could be converted into the additive noise for processing.Type analysis of the noise in the logarithmic domain was given.In the image logarithm domain,training sample blocks were selected by nonlocal method,and the principal component analysis was used to extract the main features of image blocks.A threshold principle,was proposed by linear minimum mean-square error estimate,which adapted to the signal message.The denoising images were obtained by biased estimation.Experiment results show that the presented method is valid.Compared with the existing variational methods,the new method has higher peak signal to noise ratio and better visual effect.That the performance of the proposed method is practical at a certain extent.