Ground penetrating radar (GPR) is one of the most generally used underground sensing equipment, but it is frequently contaminated by clutter and noise during data acquisition, which has a significant impact on the detection performance of buried targets. The purpose of this letter is to present a novel clutter suppression method based on the principal component Gaussian curvature decomposition (PCGCD). First, the GPR B-scan data are divided into different sub-components using principal component analysis (PCA). Then, a Gaussian curvature decomposition (GCD) method is proposed, which can be applied to PCA domain subspaces to recover more target structure information from random noise. The PCGCD method’s performance is evaluated using both numerical simulation and real-world GPR datasets. The visualization and quantitative results demonstrated our method’s superiority in protecting the underground target structure, removing complex random noise, and improving the detection ability of buried targets.

Due to the coherence of the scattering phenomenon, synthetic aperture radar (SAR) images are contaminated by multiplicative speckle noise, which seriously affects the interpretation and identification of SAR images. In this paper, we introduce the curvature filters which are realized by constructing tangent planes and correcting the regularization energy into speckle noise suppression of SAR images. Compared with the ROF model, the guided filter and some classic SAR image filtering algorithms (such as Lee filter), the curvature filters show strong competitiveness in the computing time, the peak signal-to-noise ratio (PSNR) and the structure similarity (SSIM) index. In addition, we compared the performance of the three curvature optimization models of the curvature filters, and found that the total variational filter model performs better in dealing with speckle noise of SAR images, which shows that the construction of local half-window and the assumption of piecewise constant for SAR images are robust to speckle noise suppression and structure preservation.