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

Abstract

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.