关键词: 合成孔径雷达图像, 相干斑噪声, 非下采样小波包变换, 自蛇扩散, L1-L2优化

Abstract:

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.

Key words: synthetic aperture radar image, speckle noise, undecimated wavelet packet transform, self-snake diffusion, L1-L2 optimization