LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution
文献类型：期刊论文
| 作者 | Wang, Yunlong1,2 ; Liu, Fei2; Zhang, Kunbo2; Hou, Guangqi2; Sun, Zhenan2; Tan, Tieniu2
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2018-09-01 |
| 卷号 | 27期号:9页码:4274-4286 |
| 关键词 | Implicitly Multi-scale Fusion Bidirectional Recurrent Convolutional Neural Network Light-field Super-resolution |
| DOI | 10.1109/TIP.2018.2834819 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | SUPER RESOLUTION ; CAMERAS |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000434293500008 |
| 资助机构 | National Natural Science Foundation of China(61427811 ; National Key Research and Development Program of China(2016YFB1001000 ; 61573360) ; 2017YFB0801900) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/22052]  |
| 专题 | 自动化研究所_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 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：自动化研究所
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