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Video Super-Resolution via Bidirectional Recurrent Convolutional Networks
Huang, Yan1,2; Wang, Wei1,2; Wang, Liang1,2,3
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2018-04-01
Volume40Issue:4Pages:1015-1028
SubtypeArticle
AbstractSuper resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.
KeywordDeep Learning Recurrent Neural Networks 3d Convolution Video Super-resolution
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2017.2701380
WOS KeywordIMAGE SUPERRESOLUTION ; LEARNING ALGORITHM ; NEURAL-NETWORKS ; RESOLUTION ; REGISTRATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61572504 ; Strategic Priority Research Program of the CAS(XDB02070100) ; National Key Research and Development Program of China(2016YFB1001000) ; Beijing Natural Science Foundation(4162058) ; NVIDIA DGX-1 AI Supercomputer ; NVIDIA ; 61525306 ; 61420106015 ; 61633021)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000426687100018
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14820
Collection智能感知与计算研究中心
Affiliation1.Chinese Acad Sci CASIA, Inst Automat, NLPR, Ctr Res Intelligent Percept & Comp CRIPAC, Beijing 100049, Peoples R China
2.UCAS, Beijing 100049, Peoples R China
3.Chinese Acad Sci CASIA, Inst Automat, CEBSIT, Beijing 100864, Peoples R China
Recommended Citation
GB/T 7714
Huang, Yan,Wang, Wei,Wang, Liang. Video Super-Resolution via Bidirectional Recurrent Convolutional Networks[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2018,40(4):1015-1028.
APA Huang, Yan,Wang, Wei,&Wang, Liang.(2018).Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,40(4),1015-1028.
MLA Huang, Yan,et al."Video Super-Resolution via Bidirectional Recurrent Convolutional Networks".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40.4(2018):1015-1028.
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