计算机工程与应用2016，Vol.52Issue(18)：14-17,30,5.DOI:10.3778/j.issn.1002-8331.1512-0260
基于群结构稀疏表示的图像修复
Image inpainting algorithm based on group-structured sparse representation
摘要
Abstract
Aiming at priori knowledge representation of image inpainting algorithm based on sparse representation, con-sidering image texture self-similar and group-structure spasity of atom’s coefficient, a group-structured sparse representa-tion model is proposed. In the model, the sparse coefficient of adjacent atoms is restrained by appropriate group structure, and the input image valid patches and training samples are unified for joint sparse representation and learning dictionary, in which each element of the patches has the same sparse pattern, then this relationship is used as priori knowledge for image inpainting. Experimental results on target removing and pixels lost inpainting show that the proposed method has good performance.关键词
信息处理技术/稀疏表示/联合字典学习/群结构Key words
information processing technology/sparse representation/dictionary learning/group-structured分类
信息技术与安全科学引用本文复制引用
计宏磊,杨清文..基于群结构稀疏表示的图像修复[J].计算机工程与应用,2016,52(18):14-17,30,5.基金项目
安徽省自然科学基金（No.1508085QF114）。 （）
