Sparse Representation Based Image Super-resolution Using Large Patches

doi:10.1049/cje.2018.05.011

	Sparse Representation Based Image Super-resolution Using Large Patches
	Liu Ning 1; Zhou Pan 2; Liu Wenju1 ; Ke Dengfeng1
发表期刊	CHINESE JOURNAL OF ELECTRONICS
ISSN	1022-4653
	2018-07-01
卷号	27 期号:4 页码:813-820
通讯作者	Liu Ning(liuning19880928@gmail.com)
摘要	This paper addresses the problem of generating a high-resolution image from a low-resolution image. Many dictionary based methods have been proposed and have achieved great success in super resolution application. Most of these methods use small patches as dictionary atoms, and utilize a unified dictionary pair to conduct reconstruction for each patch, which may limit the super resolution performance. We use large patches instead of small ones to combine a dictionary and to conduct patch reconstruction. Since a large patch contains more meaningful information than a small one, the reconstruction result may have more high frequency details. To guarantee the completeness of the dictionary with large patch, the scale of the dictionary should be large as well. To handle the storage and calculation problems with large dictionaries, we adopt a binary encoding method. This method can preserve local information of patches. For each patch in the low-resolution image, we search its similar patches in the low-resolution dictionary to obtain a sub-dictionary. We compute its sparse representation to get the corresponding high-resolution version. Global reconstruction constraint is enforced to eliminate the discrepancy between the SR result and the ground truth. Experimental results demonstrate that our method outperforms other super resolution methods, especially when the magnification factor is large or the image is blurred by white Gaussian noise.
关键词	Super resolution Sparse representations Binary encoding
DOI	10.1049/cje.2018.05.011
关键词[WOS]	LEARNING BINARY-CODES ; ITERATIVE QUANTIZATION ; PROCRUSTEAN APPROACH ; INTERPOLATION ; RECONSTRUCTION ; MODEL
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[91120303] ; National Natural Science Foundation of China[61273267] ; National Natural Science Foundation of China[61403370] ; National Natural Science Foundation of China[61503382] ; National Natural Science Foundation of China[61573357] ; National Natural Science Foundation of China[61573357] ; National Natural Science Foundation of China[61503382] ; National Natural Science Foundation of China[61403370] ; National Natural Science Foundation of China[61273267] ; National Natural Science Foundation of China[91120303]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000439399000020
出版者	TECHNOLOGY EXCHANGE LIMITED HONG KONG
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/26345
专题	多模态人工智能系统全国重点实验室_智能交互
通讯作者	Liu Ning
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100000, Peoples R China 2.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117583, Singapore
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Liu Ning,Zhou Pan,Liu Wenju,et al. Sparse Representation Based Image Super-resolution Using Large Patches[J]. CHINESE JOURNAL OF ELECTRONICS,2018,27(4):813-820.
APA	Liu Ning,Zhou Pan,Liu Wenju,&Ke Dengfeng.(2018).Sparse Representation Based Image Super-resolution Using Large Patches.CHINESE JOURNAL OF ELECTRONICS,27(4),813-820.
MLA	Liu Ning,et al."Sparse Representation Based Image Super-resolution Using Large Patches".CHINESE JOURNAL OF ELECTRONICS 27.4(2018):813-820.