基于MCA分解和样本聚类的超分辨率算法  

The SR algorithm based on MCA decomposition and sample clustering

作　　者：陶永耀 赵新中 梁婉文 石敏[2] 

机构地区：[1]炬芯(珠海)科技有限公司,珠海519085 [2]暨南大学信息科学技术学院,广州510632

出　　处：《电子测量技术》2016年第6期57-60,共4页Electronic Measurement Technology

基　　金：广东省科技计划(2013B090800022);广东省科技计划(2015B090901047)资助项目

摘　　要：针对SCSR(sparse coding sparse representation)算法采用通用的过完备字典无法表征多种结构类型的图像以及全局稀疏重构引入过多冗余这2个缺点,提出了基于MCA(morphological component analysis)分解和样本聚类的超分辨率算法。本算法首先采用K-means方法对待训练特征块进行聚类,得到多特征字典对来处理不同类型的低分辨率图像。然后在重构阶段,采用MCA方法提取图像的纹理成分进行稀疏重构,对平滑成分进行插值放大。实验结果表明与其他先进的算法相比,该算法能够更好地恢复图像的边缘细节,重构质量更好。In SCSR （sparse coding sparse representation ） algorithm ,the universal overcompleted dictionary cannot be adapted to variety types of images and too much redundancy is introduced by global sparse reconstruction .To overcome these shortcomings ,the SR algorithm based on MCA （morphological component analysis ） decomposition and sample clustering is proposed . Firstly , the training feature patchs are clustered by K-means algorithm , and then each clustering is trained to get dictionaries ,which are used to process variety types of images .Secondly ,the image is decomposed into texture component and smooth component by MCA method .The texture component is reconstructed sparsely and the smooth component is enlarged by Bicubic algorithm .Finally ,compared with other SR methods ,this algorithm can restore the image edge details better .

关 键 词：超分辨率 稀疏表示 MCA分解 样本聚类 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]