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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)
作者部门机器人学研究室
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码564-567
收录类别EI ; CPCI(ISTP)
EI收录号20183905873528
WOS记录号WOS:000447628700103
产权排序1
ISBN号978-1-5386-0489-2
关键词Spatial Transformer Super-resolution Convolution
摘要

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.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21353
专题机器人学研究室
通讯作者Wang Q(王强)
作者单位1.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
推荐引用方式
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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