东南大学学报（英文版）2016，Vol.32Issue(2)：177-182,6.DOI:10.3969/j.issn.1003-7985.2016.02.008
基于稀疏表示的单帧超分辨率重建
Single frame super-resolution reconstruction based on sparse representation
摘要
Abstract
In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of-the-art counterparts in terms of both numerical indicators and visual quality.关键词
单帧超分辨率重建/稀疏表示/局部方向估计/主元分析/梯度一致性Key words
single frame super-resolution reconstruction/sparse representation/local orientation estimation/principal component analysis ( PCA)/consistency of gradients分类
信息技术与安全科学引用本文复制引用
谢超,路小波,曾维理..基于稀疏表示的单帧超分辨率重建[J].东南大学学报（英文版）,2016,32(2):177-182,6.基金项目
The National Natural Science Foundation of China ( No.61374194, No.61403081), the National Key Science&Technolo-gy Pillar Program of China ( No.2014BAG01B03), the Natural Science Foundation of Jiangsu Province ( No. BK20140638), the Priority Aca-demic Program Development of Jiangsu Higher Education Institutions （ No.61374194, No.61403081）
