SURE准则的非局部SAR图像相干斑抑制  

SAR Image Despeckling Via Modified Non-Local Means: Based on SURE Criterion

作　　者：颜学颖[1] 焦李成[1] 

机构地区：[1]西安电子科技大学智能感知与图像理解教育部重点实验室,西安710071

出　　处：《电子科技大学学报》2014年第1期42-48,共7页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(61173093;61072106;61003198;61001206);教育部长江学者和创新团队支持计划(IRT1170)

摘　　要：针对传统空域非局部平均方法在合成孔径雷达图像相干斑抑制中存在相似区域提取和方向信息捕获不足的问题,提出了一种基于各向异性高斯方向窗和Stein’s无偏风险估计(SURE)准则融合的非局部均值(NLM)算法。该方法设计多个不同方向的各向异性高斯窗来匹配SAR图像的局部空间几何结构,比传统的方形窗能更好地保护SAR图像中的方向性结构。采用比率测度来衡量图像块间的相似程度,并计算基于该各向异性高斯窗的NLM结果。结合SURE准则来融合不同方向的各向异性高斯窗的非局部平均结果,获得最终的SAR图像降斑结果。针对多幅SAR图像进行对比实验,实验结果表明:该方法在有效抑制SAR图像相干斑的同时能很好地保留图像的几何结构信息,为后续的SAR图像理解与解译提供了良好的基础。Aimed at the shortage of similar region capture and directional information obtainment for SAR image despeckling using conventional non-local means method （NLM）, a new NLM SAR image despeckling method is proposed based on multiple different directional anisotropic Gaussian directional window and Stein unbiased risk estimation （SURE） aggregation. The ratio measurement strategy is utilized to compute the similarity of two patches and the NLM result is computed based on the anisotropic Gaussian windows with some direction. The results of NLM with different anisotropic Gaussian windows are aggregated by using the Stein unbiased risk estimation criterion to obtain the final SAR despeckling result. For multiple SAR images, the experiment results show that the new method has advantages in the SAR image despeckling performance, and can well preserve the local geometric structure information, which is essential for understanding and interpretation of SAR image.

关 键 词：各向异性高斯窗 非局部均值 SAR图像降斑 Stein无偏风险估计 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]