A novel synthetic aperture radar (SAR) image de-noising method based on the local pixel grouping (LPG) principal component analysis (PCA) and guided filter is proposed. This method contains two steps. In the first step, we process the noisy image by coarse filters, which can suppress the speckle effectively. The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction. Then, we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching. The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation, so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain. In the second step, we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering. Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM) index and the equivalent number of looks (ENLs), and is of perceived image quality.

中文翻译：

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通过基于分组的PCA和引导滤波器对SAR图像进行降噪

提出了一种基于局部像素分组（LPG）主成分分析（PCA）和导引滤波器的合成孔径雷达（SAR）图像去噪方法。此方法包含两个步骤。第一步，我们通过粗滤镜处理噪声图像，可以有效地抑制斑点。通过带有偏差校正的对数变换将原始SAR图像转换为加性噪声模型。然后，我们使用像素及其最近邻作为向量，通过LPG基于块相似匹配从本地窗口中选择训练样本。LPG方法可确保在PCA变换估计的局部统计计算中仅使用相似的样本块，以便在PCA域中的系数缩小后可以很好地保留图像的局部特征。第二步 我们进行了导引滤波，可以有效消除粗滤波留下的小瑕疵。模拟和真实SAR图像的实验结果表明，该方法在峰值信噪比（PSNR），结构相似性（SSIM）指数和等效值方面均超过了最新的图像去噪方法。外观（ENL）的数量，并且具有可感知的图像质量。