摘要
抑制图像噪声是电气设备红外诊断技术的前提。为了有效抑制白噪声,提高诊断的准确性,提出一种用于电缆瓷套终端红外图像的基于逐层最优基小波和贝叶斯估计的自适应去噪方法。该方法首先将红外图像真彩图分解为R、G、B颜色分量图像。对每一颜色分量图像,定义小波分解尺度系数能量百分比,基于能量百分比最大的原则,自适应选取最优基小波对颜色分量图像逐层进行小波分解,并结合Bayes最优估计准则对细节小波系数进行处理,对尺度系数和处理后的小波系数进行逐层小波重构,得到去噪后的颜色分量图像。将去噪后的颜色分量图像进行合成,得到去噪后的图像。该方法能够有效地去除白噪声,并且使去噪后的图像尽可能保留细节信息。数值试验表明,与运用sym4小波进行单一小波分解去噪方法比较,运用该方法去噪后图像的信噪比(SNR)更高,最小均方误差(MSE)更小。
The suppression of image noise is the premise of infrared condition diagnosis of electrical equipment. To improve the effectiveness of suppress white noise in the infrared image of porcelain bushing cable terminal, a kind of adaptive de-noising method based on layer by layer optimal basic wavelet and Bates estimation is put forward in this paper. Firstly, this method decomposes image into R. G and B sub-image. For each one, based on the principle of energy maximum of scale coefficients, the optimal basic wavelet in every layer is selected adaptively. Next, utilizes those optimal basic wavelets to decompose the sub-image, and processing the details of wavelet coefficients combining wih the optimal Bayes estimation criterion. Then, the real sub-image is remained and white noise is removed. Finally, it compounds the de.noised R, G and B sub-image together. Simulation results indicate that the method proposed in this paper removes the white noise effectively and keeps the information of the original image at the same time. The de-noising ability of this method is better than the sym4 wavelet with higher SNR and smaller MSE.
出处
《电测与仪表》
北大核心
2016年第9期83-89,共7页
Electrical Measurement & Instrumentation
基金
国家863计划资助项目(2011AA05A120)
关键词
红外图像
最优基小波
BAYES估计
小波去噪
自适应
infrared image, optimal base wavelet, Bayes estimation, wavelet de-noising, self-daptive
