计算机工程Issue(6)：211-215,220,6.DOI:10.3969/j.issn.1000-3428.2015.06.038
基于稀疏表示的快速图像超分辨率算法
Fast Image Super-resolution Algorithm Based on Sparse Representation
摘要
Abstract
The traditional Super Resolution ( SR ) algorithm via over-complete sparse representation has several problems,such as too large training patches, long training and iteration time, and fixed sparse degree. In view of these disadvantages,a fast SR algorithm is proposed. The core of this algorithm is to estimate the scale of the training patches by introducing Fast Kernel Density Estimation( FastKDE) to get the reasonable number of training patches in the stage of dictionary learning,and to overcome the shortcomings of greed series of sparse representation algorithms with fixed sparse degree and shortens the iteration time by using improved Generalized Orthogonal Matching Pursuit( GOMP) algorithm in the stage of sparse representation. Experimental results show that compared with the traditional dictionary training algorithm,this algorithm can improve the accuracy of SR reconstruction,and the average iteration time is less.关键词
稀疏表示/压缩感知/快速核密度估计/广义正交匹配追踪/超分辨率/字典学习Key words
sparse representation/compressed sensing/Fast Kernel Density Estimation(FastKDE)/Generalized Orthogonal Matching Pursuit( GOMP)/Super Resolution( SR)/dictionary learning分类
信息技术与安全科学引用本文复制引用
曹翔,陈秀宏,潘荣华..基于稀疏表示的快速图像超分辨率算法[J].计算机工程,2015,(6):211-215,220,6.基金项目
国家自然科学基金资助项目(61373055)。 （61373055）
