纺织高校基础科学学报Issue(4)：548-552,5.
基于稀疏表示和自相似学习的图像超分辨率重构
Image super-resolution based on sparse representation and self-similarity learning
李强 1林文晓1
作者信息
- 1. 西北工业大学理学院，陕西西安710129
- 折叠
摘要
Abstract
T he reconstructed image quality of super-resolution based on sparse representation depends on the information of high-resolution image database ,the result can not be guaranteed .Super-resolution based on image structural similarity only uses the additional information contained in the given low-reso-lution image itself ,but the information is not enough to get an ideal reconstructed image .In order to use both training database and the given low-resolution image ,a new method is put forward in this paper . First ,use the sparse representation based algorithm to reconstruct the image ;and then ,use the addition-al information contained in the low resolution image to repair the achieved image in the first step ,en-hance the quality in advance .The simulation result indicates that the method perform better than the two algorithm mentioned above .关键词
超分辨率重构/稀疏表示/附加信息/自相似学习Key words
super-resolution/sparse representation/additional information/self-similarity learning分类
信息技术与安全科学引用本文复制引用
李强,林文晓..基于稀疏表示和自相似学习的图像超分辨率重构[J].纺织高校基础科学学报,2013,(4):548-552,5.
