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 multipolarimetric 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. 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. latter latter constructed by high-dimensional non-local gradient that contains the structure information of the multichannel image. 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 multipolarimetric 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.