基于自适应非局部均值的SAR图像相干斑抑制

发布时间：2018-09-08 19:18

【摘要】：针对在利用非局部均值方法对合成孔径雷达(synthetic aperture radar,SAR)图像降斑时,存在的图像块相似性度量不准确、鲁棒性不高等问题,提出了一种基于自适应非局部均值的SAR图像相干斑抑制方法。首先,定义平滑度以刻画图像不同区域的纹理复杂程度,并设计自适应匹配函数,自适应地确定图像块和搜索窗口的大小,以提高块相似性度量的准确性,并在此基础上提出了自适应非局部均值算法框架。然后,利用Gabor滤波器对图像块的相似性进行度量,以增强块相似性度量的鲁棒性,并以所提框架为依据,设计了基于Gabor滤波器的自适应非局部均值算法。实验结果表明,所提算法不仅能够有效去除相干斑噪声,而且较好地保持了图像的纹理、边缘、点目标等信息,为后期SAR图像的理解与解译奠定了良好的基础。
[Abstract]:In order to solve the problem of imprecise similarity measurement and low robustness of image blocks in speckle reduction of synthetic Aperture Radar (synthetic aperture radar,SAR) images using non-local mean method, this paper proposes a new method to reduce the speckle of synthetic aperture radar (synthetic aperture radar,SAR) images. A speckle suppression method for SAR images based on adaptive nonlocal mean is proposed. Firstly, the smoothness is defined to describe the texture complexity of different regions of the image, and an adaptive matching function is designed to determine the size of image blocks and search windows adaptively, so as to improve the accuracy of block similarity measurement. On this basis, an adaptive nonlocal mean algorithm framework is proposed. Then, the Gabor filter is used to measure the similarity of image blocks, to enhance the robustness of block similarity measurement, and based on the proposed framework, an adaptive non-local mean algorithm based on Gabor filter is designed. The experimental results show that the proposed algorithm can not only effectively remove speckle noise, but also preserve the texture, edge and point target information of the image, which lays a good foundation for the understanding and interpretation of late SAR images.
【作者单位】：装备学院研究生院;装备学院航天装备系;
【基金】：国家高技术研究发展计划(863计划)资助课题
【分类号】：TN957.52

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