基于Shearlets变换的SAR图像去噪

在合成孔径雷达(SAR)相干噪声模型基础上提出了一种基于剪切波(Shearlets)变换的SAR图像去噪算法. Shearlets变换继承了Curvele变换和Contourlet 变换的优点，既有灵活的方向选择性又易于实现，并且对于包含C2 奇异曲线或曲面的高维信号具有最优逼近特性. 该文采用Shearlets逼近SAR图像，再用基于贝叶斯估计
理论的双变量阈值函数对Shearlets变换系数进行处理得到去噪图像. 仿真结果表明，相比使用同级Contourlet双变量阈值去噪，该算法峰值信噪比提高2 dB；相比使用非下采样Contourlet变换双变量阈值算法去噪，该算法去噪后图像不仅峰值信噪比有所提高，而且更平滑，计算时间也大大减少.

De-noising of SAR Images Based on Shearlets Transform

This paper proposes a de-noising algorithm for SAR images based on Shearlets transform. Shearlets transformation is multi-scale geometric analysis which possesses the advantages of Contourlet transform and Curvelet transform. For a singular curve or surface containing C2 high-dimensional signals, it is an optimal approximation. We apply Shearlets to approach SAR images, and use a bivariate threshold according to the Bayesian estimation theory to perform image de-noising. The obtained results show an increase of 2 dB in PSNR as compared to the Contourlet-based method with a bivariate threshold. Compared with the nonsubsampled Contourlet method with a bivariate threshold, the proposed method gives a higher PSNR and smoother denoised images. In addition, computation complexity is reduced.