基于向量相似度的LPG-PCA图像去噪算法

基于主成分分析的去噪算法在进行局部像素分组时,由于噪声具有不确定性和随机性,以欧氏距离直接作为图像块相似性这一判断标准容易使得结果产生偏差。针对此问题,文中提出了一种基于向量相似度的LPG-PCA图像去噪算法,将向量相似度和欧氏距离相结合作为相似图像块的判断标准,优化了相似图像块的选取。此外,在相似图像块样本数的选取方面采用自适应的数量选取方法,使得样本数的选取更加合理,进一步提高了图像的去噪质量。实验结果表明所提算法在峰值信噪比和结构相似性方面均优于传统的LPG-PCA图像去噪算法,且对亚毫米波成像也具有一定的去噪效果。

改进的LPG-PCA的图像去噪方法

主成分分析(PCA)变换能够去除信号之间的相关性,并且在PCA域中,很容易把信号和噪声区分出来。在对目标像素块进行处理前,首先要在一定的搜索域中寻找与其结构相似的局部像素块作为训练样本,对图像进行复制,使用双参数收缩算法对复制图像进行处理,然后使用在复制图像中对应的像素块之间的欧氏距离,来代替目标像素块与局部像素块之间的相似性,减小了噪声所带来的影响,对后续的PCA变换起到了重要作用。仿真实验表明,改进的LPG-PCA方法相对于改进之前,使图像的质量有了一定提高。

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.

结合LPG&PCA的中智学图像分割

目的针对传统图像分割方法易受噪声干扰的影响,提出一种新的结合LPG&PCA(principal component analysis with local pixel grouping)的中智学图像分割方法。方法该方法首先利用中智学集合理论把图像转化成中智学图像;然后建立LPG&PCA滤波模型,利用图像中不确定性元素信息,对图像进行α-LPG&PCA滤波运算和β-增强运算,使处理后的噪声点更加平滑;最后,利用γ-均值聚类方法进行分割。结果实验结果表明,该算法可以有效地消除噪声,提高图像的峰值信噪比,在抗噪性、分割错误率等方面都有较佳的效果。结论由于本文方法将中智学集合理论应用到图像分割中,充分利用了图像中的不确定性因素,从而提高了图像分割的精度。理论分析和实验结果表明了该算法的有效性。