Single image super-resolution via mixed examples and sparse Representation

doi:10.1109/ACPR.2017.110

CORC > 兰州理工大学 > 兰州理工大学 > 党委教师工作部（人事处、教师发展中心）

	Single image super-resolution via mixed examples and sparse Representation
	Liu, Weirong 2; Shi, Changhong 2; Liu, Chaorong 3; Liu, Jie 1
	2018-12-13
会议日期	November 26, 2017 - November 29, 2017
会议地点	Nanjing, China
关键词	Optical resolving power Discriminative dictionaries Example selection Low resolution images Quantitative assessments Single images Sparse representation Super resolution Training database
DOI	10.1109/ACPR.2017.110
页码	735-740
英文摘要	Existing super-resolution (SR) methods can be divided into two classes: The external examples SR and the internal examples SR. Although these two types of methods have been achieved satisfactory results, such methods are limited by their inherent flaws. This paper proposes mixed example selection method for combining the external examples with the internal examples. We cluster the internal examples into K classes, and select the similar external examples for every cluster to enrich the training database. And then we learn K discriminative dictionaries for the K cluster examples. Finally, we reconstruct the low resolution images with the learned discriminative dictionaries. Experiments validate the effectiveness of the proposed method in terms of visual and quantitative assessments. © 2017 IEEE.
会议录	Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
会议录出版者	Institute of Electrical and Electronics Engineers Inc., United States
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117992]
专题	党委教师工作部（人事处、教师发展中心）电气工程与信息工程学院
作者单位	1.National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou; 730050, China 2.College of Electrical and Information Engineering, Lanzhou University of Technology, China; 3.Key Laboratory of Gansu, Advanced Control for Industrial Processes, Lanzhou University of Technology, China;
推荐引用方式 GB/T 7714	Liu, Weirong,Shi, Changhong,Liu, Chaorong,et al. Single image super-resolution via mixed examples and sparse Representation[C]. 见:. Nanjing, China. November 26, 2017 - November 29, 2017.