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.