统计先验指导的非下采样Contourlet变换域SAR图像降斑

SAR image despeckling using statistical priors in nonsubsampled contourlet transform domain

提出了一种合成孔径雷达(SAR)图像降斑方案.首先利用Gamma分布和指数分布给出了NSCT域信号系数和噪声系数分布的有效逼近形式,使缩减因子对NSCT域图像子带的冗余特性具有自适应性.然后,利用方向邻域模型给出了缩减因子先验比的计算表达式,使缩减因子对NSCT域图像子带的方向特性具有自适应性,从而提高了NSCT系数缩减的有效性.实测SAR图像的降斑结果表明该降斑方案在减少斑点噪声的同时很好地保持了图像的细节特征.与多种降斑方法相比,该方案具有更好的边缘保持和后向散射系数保持性能.

A new despeckling scheme for synthetic aperture radar （SAR） images is proposed based on the adaptive shrinkage principle in the nonsubsampled contourlet transform （NSCT） domain, First, the statistical distributions of signal and speckle noise coefficient magnitudes in the NSCT-domain image subbands are effectively approximated by Gamma distributions and exponential distributions, respectively, which can make the shrinkage factor well adapt to the high redundancy of NSCT image subbands. A new set of directional neighborhood models is then proposed to,calculate the prior ratio, making the shrinkage factor well adapt to the flexible directionality of NSCT-domain image subbands, thus enhancing the coefficient shrinkage performance. The experimental results on a real SAR image demonstrate that the proposed despeckling scheme can preserve the details while speckle noise is reduced. Compared with several classical despeckling methods, the new scheme has better edge preservation performance and backscattering coefficient preservation performance.