基于小波域统计建模及显著性修正的SAR图像相干斑抑制

SAR Speckle Denoising Based on Statistic Model Combined with Medication to Significant Wavelet Significant Coefficient

该文提出了一种基于小波域统计建模与小波系数显著性修正相结合的斑点噪声滤波方法。这种方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,对对数变换后的图像进行小波变换并对小波域的高频子带系数用混合高斯模型与隐马尔可夫树模型进行建模,并采用EM算法来估计模型参数。在模型参数估计的基础上;利用贝叶斯最小均方误差准则来估计“干净”的小波系数。在此基础上引入基于显著性准则的小波系数修正,最后通过小波逆变换与指数变换获得抑制斑点噪声后的图像。用真实SAR图像实验表明,该文提出的方法能够有效地抑制斑点噪声,同时能够很好地保存边缘细节结构与强散射中心。

This paper proposes a new method based on statistical model of wavelet coefficients combined with modification to them according to significant coefficient rule. In the method, wavelet coefficients of logarithmic image are firstly modeled as mixture density of t-;vo Gaussian distributions with zero mean. In order to incorporate the spatial dependencies into the denoising procedure, Hidden Markov Tree （HMT） model is explored and Expectation Maximization （EM） algorithm is proposed to estimate model parameters. Bayes Minimum Mean Square Error （Bayes MMSE）method is used to estimate the wavelet coefficients free of noise. The wavelet coefficients are updated according to a rule whether the coefficient is a significant one or not. 2D inverse DWT and exponential transform are performed on the updated coefficients to get denoised SAR image. Experimental Results using real SAR images demonstrate that the method can not only reduce the speckle but also preserve edges and radiometric scatter points.