%0 Journal Article
%T SAR images de-speckling algorithm via an adaptive over-complete learning dictionary<br>自适应超完备字典学习的SAR图像降噪
%A Yang Meng
%A Zhang Gong
%A <br>杨萌
%A 张弓
%J 中国图象图形学报
%D 2012
%I 
%X In this paper,a de-speckling algorithm for SAR images using an adaptive over-complete learning dictionary is proposed.The algorithm is based on sparse representation of SAR images via an over-complete dictionary It has strong data sparseness and provides solid modeling assumptions for data sets.First,a practical optimization strategy based on statistical properties of the speckle noise is used to design a redundant dictionary via an iterative loop.Second,the SAR image is projected into a high dimensional space using the learning dictionary and a sparse representation of the SAR image is obtained.Third,a model for multi-objective optimization problem is built by a regulation method.Finally,the de-noising process is realized through a solution of the multi-objective optimization problem in which the mean backscatter power is reconstructed.The experimental results demonstrate that the proposed algorithm has good de-speckling capability while preserving image details.
%K speckle noise
%K SAR image
%K dictionary learning
%K sparse representation
%K orthogonal matching pursuit algorithm<br>相干斑噪声
%K 合成孔径雷达图像
%K 字典学习
%K 稀疏表示
%K 正交匹配追踪法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=D72BFD31F1937B1BF08DFFC134D596E8&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=E158A972A605785F&sid=6341CCF6B158C5F9&eid=3B2BF7AC5674E8E2&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=19