Knowledge Commons of Institute of Automation,CAS
Video super-resolution based on spatial-temporal recurrent residual networks | |
Yang, Wenhan1; Feng, Jiashi2; Xie, Guosen3; Liu, Jiaying1; Guo, Zongming1; Yan, Shuicheng4 | |
发表期刊 | COMPUTER VISION AND IMAGE UNDERSTANDING |
2018-03-01 | |
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 |
DOI | 10.1016/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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/21997 |
专题 | 多模态人工智能系统全国重点实验室_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 |
推荐引用方式 GB/T 7714 | 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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