Fast and efficient variational method based on G0 distribution for SAR image despeckling

Speckle noise is one of the major challenges that affects Synthetic Aperture Radar (SAR) images in view of its multiplicative nature. To deal with this issue, a new fast and effective despeckling algorithm is proposed, wich is based on a variational model incluing data fidelity and regularization terms. The G0 distribution is considered to define the data fidelity term, whereas the regularization term is formed by a combination of the weighted second-order total variation, the Overlapping Group Sparsity (OGS), and a box constraint. Moreover, a new fast and efficient diffusion function is proposed to solve the problem of over-smoothing, and speed up the despeckling process. The obtained results show that the proposed solution can achieve a maximum value of Equivalent Number of Looks (ENL $\simeq $ 189) for real SAR images, and the best CPU time consumption compared with the state-of-the-art speckle removal methods.