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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
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2018-09-01
Volume27Issue:9Pages:4274-4286
SubtypeArticle
AbstractThe 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.
KeywordImplicitly Multi-scale Fusion Bidirectional Recurrent Convolutional Neural Network Light-field Super-resolution
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2018.2834819
WOS KeywordSUPER RESOLUTION ; CAMERAS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61427811 ; National Key Research and Development Program of China(2016YFB1001000 ; 61573360) ; 2017YFB0801900)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000434293500008
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22052
Collection智能感知与计算研究中心
Affiliation1.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
Recommended Citation
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.
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