改进非局部均值滤波的SAR图像降噪

在非局部均值滤波(NLMF)的基础上,通过预生成相似集与2DPCA(two-dimensional principle component analysis)对NLMF进行改进,提出一种新的SAR(synthetic aperture radar)图像降噪方法。在NLMF算法框架下,针对SAR图像噪声的特点,首先经预处理选择邻近的子图像生成相似集,然后通过2DPCA提取子图像的主要特征,此过程减小了斑点噪声对相似性度量的影响,最后在降维后子图像的基础上进行相似性度量。通过仿真SAR图像和真实SAR图像的降噪实验,将本文方法与经典Lee滤波、Kuan滤波、Gamma-Map滤波和NLMF滤波相比较,结果表明,该方法无论在边缘保持还是一致区域的平滑上,都能取得较好的效果,是一种有效的SAR图像降噪算法。

SAR image denoising via improved non-local means filter

Based on the non-local means filter (NLMF), we propose an improved denoising algorithm for synthetic aperture radar (SAR)images. In the framework of the NLMF, combined with the characteristics of SAR images, we improve the NLMF using pre-generated similar sets and the two-dimensional principal component analysis (2D-PCA). First, we choose suitable image slices to generate the similar set, and then extract the main features of these image slices by applying the 2D-PCA, which can reduce the effect of the speckle noise on the similarity. Finally, we measure the similarity of the image slices based on the processed similar set. In the end, we show the noise reduction experiments of the simulated SAR images and the real SAR images. Compared with traditional Lee filter, Kuan filter, Gamma-Map filter, and the NLMF algorithms, the experiments confirm that our algorithm can achieve a better result on both: the edge retention and the smoothness of the consistency area. Simultaneously, the image quality is improved in all aspects.