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
刊名	CHINESE JOURNAL OF ELECTRONICS
	2018-07-01
卷号	27 期号:4 页码:813-820
关键词	Super resolution Sparse representations Binary encoding
ISSN号	1022-4653
DOI	10.1049/cje.2018.05.011
通讯作者	Liu Ning(liuning19880928@gmail.com)
英文摘要	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.
资助项目	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]
WOS关键词	LEARNING BINARY-CODES ; ITERATIVE QUANTIZATION ; PROCRUSTEAN APPROACH ; INTERPOLATION ; RECONSTRUCTION ; MODEL
WOS研究方向	Engineering
语种	英语
出版者	TECHNOLOGY EXCHANGE LIMITED HONG KONG
WOS记录号	WOS:000439399000020
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/26344]
专题	中国科学院自动化研究所
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
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.