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Large receptive field convolutional neural network for image super-resolution
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 Name2017 IEEE International Conference on Image Processing (ICIP)
Conference DateSeptember 17-20, 2017
Conference PlaceBeijing, China
Author of SourceThe Institute of Electrical and Electronics Engineers Signal Processing Society
Source Publication2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
PublisherIEEE
Publication PlaceNew York
2017
Pages958-962
Indexed ByEI ; CPCI(ISTP)
EI Accession number20181605019641
WOS IDWOS:000428410701017
Contribution Rank1
ISSN1522-4880
ISBN978-1-5090-2175-8
KeywordSuper Resolution Convolutional Neural Network Receptive Field Multi-scale
AbstractThis paper presents a new approach to Single Image Super Resolution (SISR), based upon Convolutional Neural Network (CNN). Although the SISR is ill-posed which can be seen as finding a non-linear mapping from a low to high dimensional space. Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration and non-linear mapping problems. We consider the single image Super-Resolution (SR) problem as convolution operators and develop a CNN to capture the characteristics of Low-Resolution (LR) input image. We find that increasing the receptive field shows the improvement in accuracy. Our solution is to establish the connection between traditional optimization-based schemes and neural network architectures. In the paper a novel, separable structure is introduced as a reliable support for robust convolution against artifacts. Our proposed method performs better than existing methods in terms of accuracy and visual improvements in our results are easily noticeable.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21350
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 Science, Shenyang 110016, China
2.Graduate University of the Chinese Academy of Science, Beijing 100049, China
Recommended Citation
GB/T 7714
Wang Q,Fan HJ,Cong Y,et al. Large receptive field convolutional neural network for image super-resolution[C]//The Institute of Electrical and Electronics Engineers Signal Processing Society. New York:IEEE,2017:958-962.
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