Non-local Sparse Models for SAR Image Despeckling

摘要：

　　This paper propose a non-local sparse model for SAR image despeckling.Sparse coding models and non-local means have been both proven very effective in natural image restoration tasks.While self-similarities exist widely in SAR images,which encourages combining these two approaches together for SAR image despeckling tasks.A grouped-sparsity regularizer is imposed to enforce similar image patches to admit similar estimates.Image adaptive dictionary is learned by block-coordinate descent algorithm.Considering the importance of point targets,a new term is integrated into sparse coding models for preserving of point targets.Experimental results show the effectiveness of the proposed algorithm in SAR image despeckling task.

关键词： SAR images non-local sparse coding despeckling dictionary learning point targets preserving

作者: Jiang Jiang Liangwei Jiang Nong Sang

作者单位: Institute for Pattern Recognition and Artificial Intelligence,Science and Technology on Multi-spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan, P. R. China

会议类型: 国际会议

会议名称: 2012年遥感中的计算机视觉国际会议

会议地点: 厦门

会议语种:英文

页码: 1-7

在线出版日期: 2012-12-16（万方平台首次上网日期，不代表论文的发表时间）