一种SAR图像相干斑噪声抑制新算法

New algorithm for reducing speckle noise in the SAR image

为抑制合成孔径雷达(SAR)图像乘性相干斑噪声,同时有效保护SAR图像的边缘特征,给出了相干斑噪声在小波域的一种加性转换噪声模型.并以此模型为基础,提出了一种非下采样小波包分解下自蛇扩散与改进L1-L2联合优化相结合的相干斑噪声抑制新算法.该算法利用非下采样小波包变换对SAR图像进行多层子带分解,然后对低通子带系数进行自蛇扩散滤波,并将滤波处理后的系数作为原SAR图像在小波域的局部均值估计,再以此局部均值为基础,利用改进的L1-L2联合优化对其他各高频子带系数进行自适应软阈值收缩滤波去噪.最后通过重构滤波后的各子带系数实现SAR图像相干斑噪声抑制.实验表明:与经典的空域Kuan滤波算法、P-M扩散滤波算法及基于非下采样小波变换的Γ-WMAP算法相比,本算法在SAR图像的相干斑噪声抑制与边缘保护方面均取得了较好的效果.

To reduce speckle noise and preserve edge characteristics in synthetic aperture radar（SAR） images,an additive transform noise mode of speckle noise in the SAR image is given and a new algorithm for speckle reduction by the combination of self-snake diffusion and regulated L1-L2 optimization under undecimated wavelet packet transform（uWPT） is proposed.In the new method,a SAR image is first decomposed into multiple subbands by multi-level uWPT.The lowpass subband is filtered by self-snake diffusion,and the subband filtered is regarded as the local mean of the original SAR image in the wavelet domain.Based on the local mean,the adaptive and shrinkage soft-thresholding filter is applied to the remaining subbands by regulated L1-L2 optimization.Finally,the despeckled image is recovered from all of filtered subbands by the inverse uWPT.Experimental results show that compared with the Kuan filter algorithm,the P-M diffusion filter algorithm and the Γ-WMAP algorithm using undecimated wavelet transform,the proposed algorithm has better performance in terms of reducing speckle noise and preserving the edge of SAR images.