Sparse representation and adaptive mixed samples regression for single image super-resolution

doi:10.1016/j.image.2018.06.001

	Sparse representation and adaptive mixed samples regression for single image super-resolution
	Zhang, Chaopeng 1; Liu, Weirong1 ; Liu, Jie2 ; Liu, Chaorong3 ; Shi, Changhong 1
	2018-09
发表期刊	SIGNAL PROCESSING-IMAGE COMMUNICATION
ISSN	0923-5965
卷号	67 页码:79-89
摘要	The example-based super-resolution (SR) methods can be mainly categorized into two classes: the internal SR methods and the external SR methods. The internal SR methods only use samples obtained from a single low resolution (LR) input, while the external SR methods only utilize an external database. The complementary information included in internal and external samples is rarely taken into account. This paper presents a novel extraction and learning method about the complementary information between external samples and internal samples, and then the learned complementary information is used to improve the single image SR performance. Firstly, we construct an initial high resolution (HR) image via sparse coding over the learned dictionary pair with external samples. Secondly, we propose an adaptive sample selection scheme (ASSS) to acquire the mixed samples. Thirdly, we present a novel adaptive mixed samples ridge regression (AMSRR) model to effectively learn the complementary information included in the mixed samples. Finally, we optimize the SR image. Extensive experimental results validate the effectiveness of the proposed algorithm comparing with the state-of-the-art methods.
关键词	Adaptive mixed samples Ridge regression Sparse representation Super-resolution
DOI	10.1016/j.image.2018.06.001
收录类别	SCI ; SCIE
语种	英语
资助项目	National Natural Science Foundation of China[61461028] ; Natural Science Foundation of Gansu Province[1508RJZA092] ; Gansu Province Basic Research Innovation Group Project[1506RJIA031]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000441487200008
出版者	ELSEVIER SCIENCE BV
EI入藏号	20182605358312
EI主题词	Learning systems
EI分类号	741.1 Light/Optics - 922.2 Mathematical Statistics
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/32477
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
通讯作者	Liu, Weirong
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China; 2.Lanzhou Univ Technol, Natl Demonstrat Ctr Expt Elect & Control Engn Edu, Lanzhou 730050, Gansu, Peoples R China; 3.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Gansu, Peoples R China
第一作者单位	电气工程与信息工程学院
通讯作者单位	电气工程与信息工程学院
第一作者的第一单位	电气工程与信息工程学院
推荐引用方式 GB/T 7714	Zhang, Chaopeng,Liu, Weirong,Liu, Jie,et al. Sparse representation and adaptive mixed samples regression for single image super-resolution[J]. SIGNAL PROCESSING-IMAGE COMMUNICATION,2018,67:79-89.
APA	Zhang, Chaopeng,Liu, Weirong,Liu, Jie,Liu, Chaorong,&Shi, Changhong.(2018).Sparse representation and adaptive mixed samples regression for single image super-resolution.SIGNAL PROCESSING-IMAGE COMMUNICATION,67,79-89.
MLA	Zhang, Chaopeng,et al."Sparse representation and adaptive mixed samples regression for single image super-resolution".SIGNAL PROCESSING-IMAGE COMMUNICATION 67(2018):79-89.

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