文献类型：期刊文献

英文题名：Research on the single image super-resolution method based on sparse Bayesian estimation

作者：Yang, Yong-qiang[1]

第一作者：杨永强

通讯作者：Yang, YQ[1]

机构：[1]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450002, Henan, Peoples R China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450002, Henan, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

年份：2019

卷号：22

起止页码：1505-1513

外文期刊名：CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS

收录：;EI(收录号：20181504999010);Scopus(收录号：2-s2.0-85045040572);WOS:【SCI-EXPANDED(收录号:WOS:000480653200127)】；

语种：英文

外文关键词：Single image super-resolution; Super-resolution; Bayesian estimations; Regression; Sparse representation

摘要：Aiming at the problem that the super-resolution effect on different low-resolution images of existing super-resolution algorithms have large difference, a novel single image super-resolution method based on sparse Bayesian estimation is proposed. In this method, the single image super-resolution problem is regarded as a regression problem. The Kronecker pulse functions are adopted as the regression basis functions, and the optimal sparse solution of the specific prediction is obtained by combining the local information and global information of the image. The Bayesian method is adopted to estimate the weights, so as to reconstructe the super-resolution image. The experimental results show that our proposed method can obtain high average peak signal to noise ratio, small variance and less time-consuming, when carried out on 14 testing images for single image super-resolution. It is proved that our proposed method has good super-resolution effect, strong adaptability, and high efficiency.