Speckle reduction model for synthetic aperture radar images based on Beltrami regularization

Abstract

A variational model is proposed to despeckle the multiplication noise in synthetic aperture radar (SAR) images to provide a high-quality interpretation of SAR data. The model consists of the data fidelity term and Beltrami regularization term. The former ensures the convexity of the model and avoids the nonlinear image transformation. The latter restrains the staircase-like artifacts caused by total variation regularization and effectively preserves the geometric structure of the image. The despeckled image is formulated as an optimization solution of the energy functional and the primal-dual algorithm is adopted to efficiently find the global minimum. Experiments on both synthetic and real SAR data demonstrate that, compared with five state-of-the-art methods, the proposed method achieves competitive results, both visually and quantitatively.