Local spatial information for image super-resolution
文献类型：期刊论文
| 作者 | Zareapoor, Masoumeh1; Jain, Deepak Kumar2; Yang, Jie1
|
| 刊名 | COGNITIVE SYSTEMS RESEARCH
 |
| 出版日期 | 2018-12-01 |
| 卷号 | 52页码:49-57 |
| 关键词 | Super-resolution Deep convolutional neural network Spatial features Image processing |
| ISSN号 | 1389-0417 |
| DOI | 10.1016/j.cogsys.2018.06.007 |
| 通讯作者 | Yang, Jie(jieyang@sjtu.edu.cn) |
| 英文摘要 | Image Super resolution plays a crucial role in many applications, such as medical imaging, remote sensing, and security surveillance. Recently convolutional neural network are becoming mainstream in computer vision. Most CNN based super resolution methods cannot fully exploit the entire feature from the original image, and thus the corresponding results will appear low resolution. In this paper, we propose a new network which can reconstruct a high resolution images by upscaling the low resolution images layer by layer with a small scale factor. This strategy helps network to possibly avoid of losing information. The existing CNN models involved bicubic interpolation for preprocessing, which leads to large feature maps and high computational loads. To settle of this problem, the proposed network directly extracts features from the input images, without using preprocessing. In addition, the proposed network investigates the spatial information which is represented by dissimilarities between a low resolution image and its corresponding high resolution by adopting a global residual learning. This differentiable strategy is inserted into the proposed network, to dynamically extract the feature maps. The proposed model not only achieves a compatible performance with the existing prominent methods but also, efficiently reduce the computational expenses. (C) 2018 Elsevier B.V. All rights reserved. |
| 资助项目 | NSFC, China[61572315] ; Committee of Science and Technology, Shanghai, China[17JC1403000] |
| WOS研究方向 | Computer Science ; Neurosciences & Neurology ; Psychology |
| 语种 | 英语 |
| WOS记录号 | WOS:000450854400006 |
| 出版者 | ELSEVIER SCIENCE BV |
| 资助机构 | NSFC, China ; Committee of Science and Technology, Shanghai, China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25715]  |
| 专题 | 离退休人员 |
| 通讯作者 | Yang, Jie |
| 作者单位 | 1.Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zareapoor, Masoumeh,Jain, Deepak Kumar,Yang, Jie. Local spatial information for image super-resolution[J]. COGNITIVE SYSTEMS RESEARCH,2018,52:49-57. |
| APA | Zareapoor, Masoumeh,Jain, Deepak Kumar,&Yang, Jie.(2018).Local spatial information for image super-resolution.COGNITIVE SYSTEMS RESEARCH,52,49-57. |
| MLA | Zareapoor, Masoumeh,et al."Local spatial information for image super-resolution".COGNITIVE SYSTEMS RESEARCH 52(2018):49-57. |
入库方式： OAI收割
来源：自动化研究所
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