SAR image denoising via improved non-local means filter
改进非局部均值滤波的SAR图像降噪

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.