AIF-LFNet: All-in-Focus Light Field Super-Resolution Method Considering the Depth-Varying Defocus
文献类型：期刊论文
| 作者 | Zhou, Shubo3; Hu, Liang1; Wang, Yunlong2 ; Sun, Zhenan2; Zhang, Kunbo2; Jiang, Xue-Qin3
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| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| 出版日期 | 2023-08-01 |
| 卷号 | 33期号:8页码:3976-3988 |
| 关键词 | Light field imaging super-resolution depth-varying defocus dataset convolutional neural network |
| ISSN号 | 1051-8215 |
| DOI | 10.1109/TCSVT.2023.3237593 |
| 通讯作者 | Sun, Zhenan(znsun@nlpr.ia.ac.cn) |
| 英文摘要 | As an aperture-divided computational imaging system, microlens array (MLA) -based light field (LF) imaging is playing an increasingly important role in computer vision. As the trade-off between the spatial and angular resolutions, deep learning (DL) -based image super-resolution (SR) methods have been applied to enhance the spatial resolution. However, in existing DL-based methods, the depth-varying defocus is not considered both in dataset development and algorithm design, which restricts many applications such as depth estimation and object recognition. To overcome this shortcoming, a super-resolution task that reconstructs all-in-focus high-resolution (HR) LF images from low-resolution (LR) LF images is proposed by designing a large dataset and proposing a convolutional neural network (CNN) -based SR method. The dataset is constructed by using Blender software, consisting of 150 light field images used as training data, and 15 light field images used as validation and testing data. The proposed network is designed by proposing the dilated deformable convolutional network (DCN) -based feature extraction block and the LF subaperture image (SAI) Deblur-SR block. The experimental results demonstrate that the proposed method achieves more appealing results both quantitatively and qualitatively. |
| WOS关键词 | RECONSTRUCTION ; DECONVOLUTION ; NETWORK |
| 资助项目 | National Natural Science Foundation of China[61803372] ; National Natural Science Foundation of China[61806197] ; National Natural Science Foundation of China[U1836217] ; National Natural Science Foundation of China[62006225] ; National Natural Science Foundation of China[62071468] ; Fundamental Research Funds for the Central Universities[2232021D-34] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001045167400034 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53966]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Sun, Zhenan |
| 作者单位 | 1.Aerosp Informat Res Inst, Chinese Acad Sci, Key Lab Computat Opt Imaging Technol, Beijing Beijing, Peoples R China 2.Chinese Acad Sci CASIA, Inst Automat, Ctr Res Intelligent Percept & Comp, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Donghua Univ, Inst Informat Sci & Technol, Shanghai 201620, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Shubo,Hu, Liang,Wang, Yunlong,et al. AIF-LFNet: All-in-Focus Light Field Super-Resolution Method Considering the Depth-Varying Defocus[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2023,33(8):3976-3988. |
| APA | Zhou, Shubo,Hu, Liang,Wang, Yunlong,Sun, Zhenan,Zhang, Kunbo,&Jiang, Xue-Qin.(2023).AIF-LFNet: All-in-Focus Light Field Super-Resolution Method Considering the Depth-Varying Defocus.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,33(8),3976-3988. |
| MLA | Zhou, Shubo,et al."AIF-LFNet: All-in-Focus Light Field Super-Resolution Method Considering the Depth-Varying Defocus".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 33.8(2023):3976-3988. |
入库方式： OAI收割
来源：自动化研究所
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