关键词: 图像去噪, 合成孔径雷达, 核范数, 奇异值分解

Abstract: A synthetic aperture radar (SAR) image denoising method based on fast weighted nuclear norm minimization is proposed. Firstly, multiplicative speckle noise is converted to additive noise by logarithmic transformation. Secondly, the nonlocal similarity is used for image block matching. Next, according to the framework of the low rank model, random singular value decomposition is introduced to replace the singular value decomposition in the weighted nuclear norm minimization (WNNM) algorithm for approximating the low rank matrix. Then to enhance the texture of the image, the gradient histogram preservation method is used. Finally, fast denoising of SAR images is achieved. Experiments on the MSTAR database show that the proposed approach is effective in SAR image denosing and the edge preserving in comparison with some traditional algorithms. Moreover, it is three times faster than the WNNM method.

Key words: image denoising, synthetic aperture radar (SAR), nuclear norm, singular value decomposition