基于复小波噪声方差显著修正的SAR图像去噪

SAR Image Denoising Based on Significant Estimation of Noise Variance of Complex Wavelet Coefficients

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Syn-thetic Aperture Radar,SAR)图像斑点噪声滤波方法。该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,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。

We proposed an algorithm of SAR speckle denoising method based on combination of statistic model of complex wavelet coefficients and significant estimation of noise variance. This method first employs a logarithmic transformation to change the multiplicative speckle into additive noise,then logarithmic image is processed by Dual-Tree Complex Wavelet Transform,and the model ,which is based on statistical distribution for the complex wavelet coefficients of SAR image,is set up. Under such prior distribution, Maximum A Posteriori（MAP） estimator is used to restore the wavelet coefficients from the noisy observations to achieve the goal of filtering noise. Experiment results show that the method can remove the noise while preserving significant image details and obtain the good performance.