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Single Image Super-resolution Using Spatial Transformer Networks
Wang Q(王强); Fan HJ(范慧杰); Cong Y(丛杨); Tang YD(唐延东)
Department机器人学研究室
Conference Name7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Conference DateJuly 31 - August 4, 2017
Conference PlaceHawaii, USA
Author of SourceIEEE Robotics and Automation Society
Source Publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages564-567
Indexed ByEI ; CPCI(ISTP)
EI Accession number20183905873528
WOS IDWOS:000447628700103
Contribution Rank1
ISBN978-1-5386-0489-2
KeywordSpatial Transformer Super-resolution Convolution
Abstract

Most of the previous models performed well for Single Image Super-Resolution (SISR). In these methods, the Low Resolution (LR) input image is amplified to the size of High Resolution (HR) through bicubic interpolation. However, bicubic interpolation can not represent the high frequency features of images with only one filter. Therefore, in this paper, we used a original framework which can effectively extract the feature maps from the input image space and transform to HR feature maps based on Spatial Transformer Networks (STN). In our STN-SR method, there are three kinds of parameters should be learned from the model: (i) a serial of filters to extract LR image feature maps; (ii)a local small network to learn parameters of the transformation 􀀀_(G) and (iii) the filter parameters to restore the HR patchs from the input HR feature maps through a restoring layer. Our model directly focus on the whole image, the proposed STN-SR method does not clip the image into many small size patches, and can use the image gobal message to rebuild more robust local texture. Compared to privious SR methods, the proposed STN-SR method can gain completely real image, while illustrating better edge and texture preservation performance.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21353
Collection机器人学研究室
Corresponding AuthorWang Q(王强)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Wang Q,Fan HJ,Cong Y,et al. Single Image Super-resolution Using Spatial Transformer Networks[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:564-567.
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