摘要
为了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,提出一种基于MAP估计的复小波域局部自适应去噪方法。首次证实了绝缘子红外热像双树复小波变换(DT-CWT)系数服从拉普拉斯分布,并对不同滤波器组采用各自最精细分解层子带系数估计噪声方差,利用待估计点圆形邻域系数估计信号方差,且随分辨率变化调整圆形邻域半径,使得MAP估计的无噪声系数更为准确,提高了去噪图像质量。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DT-CWT的贝叶斯阈值去噪方法具有更高的信噪比,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 that is strongly interfered by white-noise, a complex wavelet-domain local adaptive denoising method based on maxi- mum a posteriori （MAP） estimation is developed. It is confirmed for the first time that the dual tree complex wavelet transform （DT-CWT） coefficients of insulator infrared thermal image obey Laplacian distribution. The authors utilize the finest scaling sub-band coefficients of different filter banks to estimate their respective noise variances, and compute the signal variance of the coefficient using neighboring coefficients within a circular window whose radius varies with resolution, so noise-free coefficients are more accurately estimated by MAP estimation and the quality of the denoised image is improved. Experimental results demonstrate that the proposed method gets higher signal-to-noise rate （SNR）, de-noises more effectively and preserves more detailed information of the original image than traditional Wiener filtering method, the adaptive Bayesian threshold methods based on discrete wavelet transform and DT-CWT.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2009年第10期2070-2075,共6页
Chinese Journal of Scientific Instrument
基金
湖南省科技计划(2006GK3043)
国网电力科学研究院科研基金(2007-2009年)
长沙市科技计划(2007-2009年)资助项目
