Video super-resolution based on spatial-temporal recurrent residual networks
Yang, Wenhan1; Feng, Jiashi2; Xie, Guosen3; Liu, Jiaying1; Guo, Zongming1; Yan, Shuicheng4
2018-03-01
发表期刊COMPUTER VISION AND IMAGE UNDERSTANDING
卷号168页码:79-92
文章类型Article
摘要In this paper, we propose a new video Super-Resolution (SR) method by jointly modeling intra-frame redundancy and inter-frame motion context in a unified deep network. Different from conventional methods, the proposed Spatial-Temporal Recurrent Residual Network (STR-ResNet) investigates both spatial and temporal residues, which are represented by the difference between a high resolution (HR) frame and its corresponding low resolution (LR) frame and the difference between adjacent HR frames, respectively. This spatial-temporal residual learning model is then utilized to connect the intra-frame and inter-frame redundancies within video sequences in a recurrent convolutional network and to predict HR temporal residues in the penultimate layer as guidance to benefit estimating the spatial residue for video SR. Extensive experiments have demonstrated that the proposed STR-ResNet is able to efficiently reconstruct videos with diversified contents and complex motions, which outperforms the existing video SR approaches and offers new state-of-the-art performances on benchmark datasets.
关键词Spatial Residue Temporal Residue Video Super-resolution Inter-frame Motion Context Intra-frame Redundancy
WOS标题词Science & Technology ; Technology
DOI10.1016/j.cviu.2017.09.002
关键词[WOS]IMAGE SUPERRESOLUTION ; SUPER RESOLUTION ; REGULARIZATION ; ALGORITHM
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61772043) ; State Scholarship Fund from the China Scholarship Council
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000429185700007
引用统计
被引频次:5[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/173211/21997
专题模式识别国家重点实验室_模式分析与学习
作者单位1.Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China
2.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117583, Singapore
3.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
4.Qihoo 360 Technol Co Ltd, Artificial Intelligence Inst, Beijing 100015, Peoples R China
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Yang, Wenhan,Feng, Jiashi,Xie, Guosen,et al. Video super-resolution based on spatial-temporal recurrent residual networks[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2018,168:79-92.
APA Yang, Wenhan,Feng, Jiashi,Xie, Guosen,Liu, Jiaying,Guo, Zongming,&Yan, Shuicheng.(2018).Video super-resolution based on spatial-temporal recurrent residual networks.COMPUTER VISION AND IMAGE UNDERSTANDING,168,79-92.
MLA Yang, Wenhan,et al."Video super-resolution based on spatial-temporal recurrent residual networks".COMPUTER VISION AND IMAGE UNDERSTANDING 168(2018):79-92.
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