Speckle Reduction Variational Model with Total Variation Regularization

In coherent imaging systems, such as synthetic aperture radar (SAR), the observed images are contaminated by the speckle noise (multiplicative noise). Due to the edge preserving feature of the total variation (TV), variational models with TV regularization have attracted much interest in removing the speckle noise. However, the fidelity term of the variational model, based on maximum a posteriori estimation, is not quadratic and nonconvex. In this paper, we show how to relax nonconvexity and introduce a very efficient algorithm based on the alternating minimization method to solve TV based speckle reduction variational model. The proposed method is very simple and highly parallelizable, and so it is very efficient to despeckle huge size SAR images. Numerical results show that our proposed method outperforms the state-of-the-art algorithms for speckle reduction variational models with the TV regularization in terms of the CPU time.