平稳小波域局部自适应绝缘子的红外热像去噪  

Stationary Wavelet-domain Local Adaptive Denoising Method for Insulator Infrared Thermal Image

作　　者：李佐胜[1] 姚建刚[1] 杨迎建[2] 袁田[2] 李文杰 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082 [2]国网电力科学研究院,武汉430074 [3]湖南湖大华龙电气与信息技术有限公司,长沙410012

出　　处：《高电压技术》2009年第4期833-837,共5页High Voltage Engineering

基　　金：湖南省科技计划(2006GK3043)~~

摘　　要：为从强白噪声干扰的红外热像中提取真实的绝缘子盘面温度场信息,提出了一种平稳小波域局部自适应绝缘子红外热像去噪方法。该法假设平稳小波变换子带系数服从拉普拉斯分布,利用最精细分解层子带系数估计噪声方差,使用待估计点圆形邻域系数估计信号方差,根据图像噪信比自适应调整邻域窗口大小,采用最大后验估计器局部自适应估计各高频子带小波系数,最后对处理后的小波系数进行平稳小波反变换得到去噪后图像。实验结果表明,该方法比传统的Wiener滤波法、基于离散小波变换和平稳小波变换的贝叶斯阈值去噪法的信噪比更高,在有效去除图像噪声的同时,图像细节信息保留更完好。In order to gain the real temperature distribution of insulator surface from infrared thermal image which is strongly interfered by white-noise, a stationary wavelet domain local adaptive denoising method for infrared thermal image is developed. It is assumed that the stationary wavelet transform（SWT） subband coefficients can be modeled by Laplacian distribution. The noise variance is estimated using the finest scaling subband coefficients. The pointwise signal variance is computed with its circular neighbouring coefficients, and the neighborhood size is adjusted based on the noise-to-signal ratio of image. Maximum a posteriori estimator is adopted to estimate different scaling clean coefficients locally and adaptively. Finally, inverse SWT is applied to obtain the denoised image. Simulation studies are carried out showing that the proposed method gets higher signal-to-noise ratio, denoises more effectively and preserves more detail information than traditional Wiener filtering method, the adaptive Bayesian threshold methods based on discrete wavelet transform and SWT.

关 键 词：平稳小波变换 绝缘子红外热像 局部自适应 最大后验估计 拉普拉斯分布 图像去噪 

分 类 号：TM855[电气工程—高电压与绝缘技术]