SAR image despeckling based on G<sup>0</sup> distribution and nonlocal TV regularization

Abstract

In this paper, we propose a new model for synthetic aperture radar (SAR) image despeckling based on the G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> statistical distribution and nonlocal total variation regularization. By taking the distribution of the backscatter into account, a new data fidelity term is derived by the maximum a posteriori Bayesian rule. Combining the new fidelity term with the nonlocal total variation regularization gives a new variational model for SAR image despeckling. The primal-dual algorithm framework is then used to solve the new variational problem. Experimental results on real SAR images demonstrate the validity of the proposed method.