摘要
采用无偏最小方差估计准则对不同尺度空间和不同方向上信号的小波系数进行自适应估计,以后验概率均值作为小波系数的估计,然后对小波系数进行小波逆变换得到去噪后图像。该方法最大程度的保留了图像的信息,具有良好的去噪效果。数值试验证明,与固定阈值法和极大极小阈值法比较,运用该方法去噪后的图像具有更高的信噪比(SNR)和更小的最小均方误差(MSE)。
A wavelet adaptive method for insulator infrared image denoising based on Bayes estimation is presented. An unbiased least-squares estimation rule is adopted to estimate the wavelet coefficients on different orientations of different wavelet scaling spaces adaptively. The mean of the posterior probability distribution is used as the estimations of the wavelet coefficients of different layers. Then, wavelet inverse transform is used to obtain the denoised image. Experiment results indicate that the method proposed in this paper is excellent on keeping the information of the original image. The denoising ability of this method is better than the fixed threshold method and the maximum-minimum threshold method. The eminent performance of this method is proved by its higher SNR（signal- to- noise rate）, smaller MSE（minimizes the mean squared error）.
出处
《电工技术学报》
EI
CSCD
北大核心
2006年第1期37-41,共5页
Transactions of China Electrotechnical Society
基金
国经贸技术资助项目([2002]845)
湖南省产业研发项目(湘计高技[2003]790)
湖南省电力科技攻关项目(湘电[2003]005)
关键词
BAYES估计
小波系数
去噪
自适应
Bayes estimation, wavelet coefficients, denoising, adaptive
