基于小波变换统计模型的电子元器件图像去噪算法研究  

Statistical Model Based Wavelet Transform for the Image Denoising of Electronic Components

作　　者：刘良江 毛建旭[2] 彭正梁 

机构地区：[1]湖南省计量检测研究院,湖南长沙410014 [2]湖南大学,湖南长沙410082

出　　处：《信息安全与技术》2012年第4期54-58,共5页

基　　金：国家自然基金项目基于视觉的药液产品质量在线检测方法及关键技术研究(61072121)资助

摘　　要：在先进电子制造中,为了应用机器视觉方法来完成电子元器件的检测、处理和识别,必须对采集的相关图像进行去噪处理。文章研究了一种基于小波变换统计模型的去噪算法,利用四树复小波包变换把含噪图像高频方向子图分为主要类和次要类。然后,采用非高斯双变量模型和零均值高斯分布模型分别对主要类和次要类复系数进行建模,从而实现噪声抑制功能。实验结果验证了本文方法的有效性。In advanced electronic manufacturing,the image de-noising processing is very necessary for the machine vision method,which can complete the testing,handling and identification of electronic components.In this paper,a novel image de-noising method based on wavelet packet transform is presented by using a mixed statistical model.The noisy image was decomposed into a low frequency approximation sub-image and some high frequency directional sub-images via the wavelet packet transform,and the high frequency directional sub-images are classified two categories：major coefficients and minor coefficients.So the noise in the major coefficients and the minor coefficients are removed by using of a mixed statistical model combining non-Gaussian bivariate model with zero mean Gaussian distributing model.The simulation has shown the effectiveness of the proposed method.

关 键 词：图像去噪 小波变换 电子元器件 统计模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]