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

• Published in: Chinese journal of aeronautics Vol. 28; no. 3; pp. 770 - 779
• Main Authors: Xi, Rubing, Wang, Zhengming, Xie, Meihua, Zhao, Xia, Wang, Weiwei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2015
• Subjects: Fixed point iteration; Multichannel image denoising; Non-local; SAR image despeckling; SAR图像; Vectorial total variation; 变分模型; 合成孔径雷达; 多通道; 局部向量; 相干斑抑制; 矢量数据; 迭代算法; Non-local; Multichannel image denoising; Vectorial total variation; SAR image despeckling; Fixed point iteration
• ISSN: 1000-9361
• DOI: 10.1016/j.cja.2015.03.012

Abstract This paper aims at the multichannel synthetic aperture radar （SAR） image speckle reduc- tion. 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 RAI）ARSAT-2 images despeckling. The result shows that this model performs better than the original vectorial total variational model on texture preserving.