基于Shearlets变换的SAR图像去噪  

De-noising of SAR Images Based on Shearlets Transform

作　　者：刘帅奇[1] 胡绍海[1] 肖扬[1] 

机构地区：[1]北京交通大学信息科学研究所,北京100044

出　　处：《应用科学学报》2012年第6期629-634,共6页Journal of Applied Sciences

基　　金：国家自然科学基金(No.60572093);北京市自然科学基金(No.4102050);航空科学基金与航空电子系统射频综合仿真航空科技重点实验室基金(No.201120M5007)资助

摘　　要：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.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 bivaxiate 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 non- subsampled Contourlet method with a bivariate threshold, the proposed method gives a higher PSNR and smoother denoised images. In addition, computation complexity is reduced.

关 键 词：去噪 剪切波 轮廓波 合成孔径雷达 

分 类 号：TN911.6[电子电信—通信与信息系统]