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Sparse Representation Based Image Super-resolution Using Large Patches | |
Liu Ning1; Zhou Pan2; 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 | |
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. |
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