A non-local vectorial total variational model for multichannel SAR image speckle suppression

This paper aims at the multichannel synthetic aperture radar (SAR) image speckle reduction. This paper proposes a novel energy minimized regularization model for multichannel image denoising, which is an extension of the non-local total variational model for gray-scale image. It contains two terms, namely the vectorial data fidelity term and the non-local vectorial total variation term. The latter is constructed by high-dimensional non-local gradient that contains the structure information of the multichannel image. The existence and the uniqueness of the solution of the model are proved. A fixed point iterative algorithm is designed to acquire the solution of this model. The convergence property of this algorithm is proved as well. This model is applied to the multi-polarimetric and multi-temporal RADARSAT-2 images despeckling. The result shows that this model performs better than the original vectorial total variational model on texture preserving. (C) 2015 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).