%0 Journal Article
%T Structured dictionary learning based on group sparsity<br>基于群稀疏的结构化字典学习
%A Guo Jingfeng
%A Li Xian
%A <br>郭景峰
%A 李贤
%J 中国图象图形学报
%D 2012
%I 
%X Sparse representation of signals is an evolving field in many machine learning and image processing tasks. Nowadays, more and more attention is paid on the algorithm for learning dictionaries.Traditionally, the dictionary is an unstructured set of atoms. Considering the sparsity of the group of the sparse representation signal, a mathematical model of the dictionary learning based on the group sparsity is constructed. We propose an efficient algorithm for learning structured dictionary according to the convex analysis and monotone operator theory. The experiments show that the algorithm converges faster, the dictionary trained from the new model adapts better to the data and the data is better represented, which overall improves the image enhancement effect.
%K sparse representation
%K dictionary learning
%K group sparsity
%K convex optimization
%K monotone operator<br>稀疏表示
%K 字典学习
%K 群稀疏
%K 凸优化
%K 单调算子
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=37D4B66310E46B830EEDB34AA2F9611A&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=708DD6B15D2464E8&sid=AFB21040E5F48417&eid=B3079604173FE132&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=15