Despeckling of SAR image using curvature optimization
Qibin Su, Pengyu Zhang, Liang Shen, Qin Xin, Xiaotao Huang · 2022
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.