Video Super-Resolution via Bidirectional Recurrent Convolutional Networks

doi:10.1109/TPAMI.2017.2701380

CASIA OpenIR > 模式识别实验室

	Video Super-Resolution via Bidirectional Recurrent Convolutional Networks
	Huang, Yan1,2 ; Wang, Wei1,2 ; Wang, Liang1,2,3
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2018-04-01
卷号	40 期号:4 页码:1015-1028
文章类型	Article
摘要	Super 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.
关键词	Deep Learning Recurrent Neural Networks 3d Convolution Video Super-resolution
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TPAMI.2017.2701380
关键词[WOS]	IMAGE SUPERRESOLUTION ; LEARNING ALGORITHM ; NEURAL-NETWORKS ; RESOLUTION ; REGISTRATION
收录类别	SCI
语种	英语
项目资助者	National 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研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000426687100018
引用统计	被引频次：131[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14820
专题	模式识别实验室
作者单位	1.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
第一作者单位	模式识别国家重点实验室
推荐引用方式 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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