High-fidelity View Synthesis for Light Field Imaging With Extended Pseudo 4DCNN | |
Wang, Yunlong1; Liu, Fei1; Zhang, Kunbo1; Wang, Zilei2; Sun, Zhenan1; Tan, Tieniu1 | |
发表期刊 | IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING |
ISSN | 2573-0436 |
2020 | |
卷号 | 6页码:830-842 |
摘要 | Multi-view properties of light field (LF) imaging enable exciting applications such as auto-refocusing, depth estimation and 3D reconstruction. However, limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards more practical vision applications. Existing view synthesis methods mainly break the task into two steps, i.e. depth estimating and view warping, which are usually inefficient and produce artifacts over depth ambiguities. We have proposed an end-to-end deep learning framework named Pseudo 4DCNN to solve these problems in a conference paper. Rethinking on the overall paradigm, we further extend pseudo 4DCNN and propose a novel loss function which is applicable for all tasks of light field reconstruction i.e. EPI Structure Preserving (ESP) loss function. This loss function is proposed to attenuate the blurry edges and artifacts caused by averaging effect of L-2 norm based loss function. Furthermore, the extended Pseudo 4DCNN is compared with recent state-of-the-art (SOTA) approaches on more publicly available light field databases, as well as self-captured light field biometrics and microscopy datasets. Experimental results demonstrate that the proposed framework can achieve better performances than vanilla Pseudo 4DCNN and other SOTA methods, especially in the terms of visual quality under occlusions. The source codes and self-collected datasets for reproducibility will be available online soon. |
关键词 | View synthesis light field reconstruction end-to-end structure preserving extended pseudo 4DCNN |
DOI | 10.1109/TCI.2020.2986092 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61806197] ; National Natural Science Foundation of China[61803372] ; National Key Research and Development Program of China[2016YFB1001000] ; National Key Research and Development Program of China[2017YFB0801900] ; Science and Technology Cooperation Project with Academy of Sichuan Province[18SYXHZ0015] ; Science and Technology Cooperation Project with University of Sichuan Province[18SYXHZ0015] |
项目资助者 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Science and Technology Cooperation Project with Academy of Sichuan Province ; Science and Technology Cooperation Project with University of Sichuan Province |
WOS研究方向 | Engineering ; Imaging Science & Photographic Technology |
WOS类目 | Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000560667800005 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40519 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Sun, Zhenan |
作者单位 | 1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit Inst Automat, Beijing 100190, Peoples R China 2.Univ Sci & Technol China, Hefei, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Yunlong,Liu, Fei,Zhang, Kunbo,et al. High-fidelity View Synthesis for Light Field Imaging With Extended Pseudo 4DCNN[J]. IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING,2020,6:830-842. |
APA | Wang, Yunlong,Liu, Fei,Zhang, Kunbo,Wang, Zilei,Sun, Zhenan,&Tan, Tieniu.(2020).High-fidelity View Synthesis for Light Field Imaging With Extended Pseudo 4DCNN.IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING,6,830-842. |
MLA | Wang, Yunlong,et al."High-fidelity View Synthesis for Light Field Imaging With Extended Pseudo 4DCNN".IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 6(2020):830-842. |
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2020-【TCI】-High-fide(7054KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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