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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.3042059
|2 doi
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|a pubmed24n1061.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Wang, Yingqian
|e verfasserin
|4 aut
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| 245 |
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|a Light Field Image Super-Resolution Using Deformable Convolution
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 15.12.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce beneficial angular information for image super-resolution (SR). However, it is challenging to incorporate angular information due to disparities among LF images. In this paper, we propose a deformable convolution network (i.e., LF-DFnet) to handle the disparity problem for LF image SR. Specifically, we design an angular deformable alignment module (ADAM) for feature-level alignment. Based on ADAM, we further propose a collect-and-distribute approach to perform bidirectional alignment between the center-view feature and each side-view feature. Using our approach, angular information can be well incorporated and encoded into features of each view, which benefits the SR reconstruction of all LF images. Moreover, we develop a baseline-adjustable LF dataset to evaluate SR performance under different disparity variations. Experiments on both public and our self-developed datasets have demonstrated the superiority of our method. Our LF-DFnet can generate high-resolution images with more faithful details and achieve state-of-the-art reconstruction accuracy. Besides, our LF-DFnet is more robust to disparity variations, which has not been well addressed in literature
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Yang, Jungang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Longguang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ying, Xinyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Tianhao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a An, Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Yulan
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 01., Seite 1057-1071
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g volume:30
|g year:2021
|g day:01
|g pages:1057-1071
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2020.3042059
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