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LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution
Wang, Yunlong1,2; Liu, Fei2; Zhang, Kunbo2; Hou, Guangqi2; Sun, Zhenan2; Tan, Tieniu2
2018-09-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
卷号27期号:9页码:4274-4286
文章类型Article
摘要The low spatial resolution of light-field image poses significant difficulties in exploiting its advantage. To mitigate the dependency of accurate depth or disparity information as priors for light-field image super-resolution, we propose an implicitly multi-scale fusion scheme to accumulate contextual information from multiple scales for super-resolution reconstruction. The implicitly multi-scale fusion scheme is then incorporated into bidirectional recurrent convolutional neural network, which aims to iteratively model spatial relations between horizontally or vertically adjacent sub-aperture images of light-field data. Within the network, the recurrent convolutions are modified to be more effective and flexible in modeling the spatial correlations between neighboring views. A horizontal sub-network and a vertical sub-network of the same network structure are ensembled for final outputs via stacked generalization. Experimental results on synthetic and real-world data sets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in peak signal-to-noise ratio and gray-scale structural similarity indexes, which also achieves superior quality for human visual systems. Furthermore, the proposed method can enhance the performance of light field applications such as depth estimation.
关键词Implicitly Multi-scale Fusion Bidirectional Recurrent Convolutional Neural Network Light-field Super-resolution
WOS标题词Science & Technology ; Technology
DOI10.1109/TIP.2018.2834819
关键词[WOS]SUPER RESOLUTION ; CAMERAS
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61427811 ; National Key Research and Development Program of China(2016YFB1001000 ; 61573360) ; 2017YFB0801900)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000434293500008
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/22052
专题智能感知与计算研究中心
作者单位1.Univ Sci & Technol China, Hefei 230027, Anhui, Peoples R China
2.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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Wang, Yunlong,Liu, Fei,Zhang, Kunbo,et al. LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2018,27(9):4274-4286.
APA Wang, Yunlong,Liu, Fei,Zhang, Kunbo,Hou, Guangqi,Sun, Zhenan,&Tan, Tieniu.(2018).LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution.IEEE TRANSACTIONS ON IMAGE PROCESSING,27(9),4274-4286.
MLA Wang, Yunlong,et al."LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution".IEEE TRANSACTIONS ON IMAGE PROCESSING 27.9(2018):4274-4286.
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