CASIA OpenIR
Local spatial information for image super-resolution
Zareapoor, Masoumeh1; Jain, Deepak Kumar2; Yang, Jie1
Source PublicationCOGNITIVE SYSTEMS RESEARCH
ISSN1389-0417
2018-12-01
Volume52Pages:49-57
Corresponding AuthorYang, Jie(jieyang@sjtu.edu.cn)
AbstractImage 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.
KeywordSuper-resolution Deep convolutional neural network Spatial features Image processing
DOI10.1016/j.cogsys.2018.06.007
Indexed BySCI
Language英语
Funding ProjectNSFC, China[61572315] ; Committee of Science and Technology, Shanghai, China[17JC1403000]
Funding OrganizationNSFC, China ; Committee of Science and Technology, Shanghai, China
WOS Research AreaComputer Science ; Neurosciences & Neurology ; Psychology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences ; Psychology, Experimental
WOS IDWOS:000450854400006
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25716
Collection中国科学院自动化研究所
Corresponding AuthorYang, Jie
Affiliation1.Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200240, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
Recommended Citation
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.
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