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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2834819
|2 doi
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|a eng
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| 100 |
1 |
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|a Wang, Yunlong
|e verfasserin
|4 aut
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| 245 |
1 |
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|a LFNet
|b A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution
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|c 2018
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|a Text
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|a ƒaComputermedien
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|a Date Completed 30.07.2018
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| 500 |
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|a Date Revised 30.07.2018
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Liu, Fei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Kunbo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hou, Guangqi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sun, Zhenan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tan, Tieniu
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 9 vom: 01. Sept., Seite 4274-4286
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|g volume:27
|g year:2018
|g number:9
|g day:01
|g month:09
|g pages:4274-4286
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|u http://dx.doi.org/10.1109/TIP.2018.2834819
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