用于计算机视觉任务的光场成像技术综述

贾晨; 石凡; 赵萌; 陈胜勇

doi:10.1631/FITEE.2100180

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用于计算机视觉任务的光场成像技术综述

常规文章 | Updated：2022-09-08

- 用于计算机视觉任务的光场成像技术综述
  Enhanced Publication
- Light field imaging for computer vision: a survey
- 信息与电子工程前沿(英文版) 2022年23卷第7期页码：1077-1097
- Affiliations：
  
  1.Engineering Research Center of Learning-Based Intelligent System (Ministry of Education), Tianjin University of Technology,Tianjin300384,China
  2.Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology,Tianjin300384,China
- Author bio：
  
  ‡Corresponding authors
- Funds：
  
  the National Natural Science Foundation of China(61906133;62020106004;92048301)
- DOI：10.1631/FITEE.2100180
  中图分类号： TP391.4
- 纸质出版日期：2022-07-23，
  
  网络出版日期：2022-07-12，
  
  收稿日期：2021-04-07，
  
  修回日期：2021-11-10，
- Accepted：
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贾晨, 石凡, 赵萌, 等. 用于计算机视觉任务的光场成像技术综述[J]. 信息与电子工程前沿(英文版), 2022,23(7):1077-1097.

CHEN JIA, FAN SHI, MENG ZHAO, et al. Light field imaging for computer vision: a survey. [J]. Frontiers of information technology & electronic engineering, 2022, 23(7): 1077-1097.
贾晨, 石凡, 赵萌, 等. 用于计算机视觉任务的光场成像技术综述[J]. 信息与电子工程前沿(英文版), 2022,23(7):1077-1097. DOI： 10.1631/FITEE.2100180.

CHEN JIA, FAN SHI, MENG ZHAO, et al. Light field imaging for computer vision: a survey. [J]. Frontiers of information technology & electronic engineering, 2022, 23(7): 1077-1097. DOI： 10.1631/FITEE.2100180.

摘要

光场成像因其解决计算机视觉问题的能力而备受关注。本文首先简要回顾了近年来计算机视觉的研究进展。对于影响计算机视觉发展的大多数因素来说，视觉信息获取的丰富性和准确性起着决定性作用。光场成像技术利用照相机或微透镜阵列记录光线位置和方向信息，获取完整三维场景信息，为计算机视觉研究做出巨大贡献。光场成像提高了深度估计以及图像分割、融合和三维重建的精度。光场成像还被创新地应用于虹膜和人脸识别、材料和虚假行人识别、极平面图像采集和形状恢复以及光场显微镜。我们进一步总结了光场成像技术在计算机视觉研究中存在的问题和发展趋势，如光场数据集的建立和评估、在高动态范围条件下的应用、光场增强和虚拟现实。光场成像在各种研究中取得巨大成功。在过去25年，超过180篇文献报道了光场成像在解决计算机视觉问题上的能力。我们梳理了这些文献，使研究人员更容易搜索有关解决方案的详细方法。

Abstract

Light field (LF) imaging has attracted attention because of its ability to solve computer vision problems. In this paper we briefly review the research progress in computer vision in recent years. For most factors that affect computer vision development

the richness and accuracy of visual information acquisition are decisive. LF imaging technology has made great contributions to computer vision because it uses cameras or microlens arrays to record the position and direction information of light rays

acquiring complete three-dimensional (3D) scene information. LF imaging technology improves the accuracy of depth estimation

image segmentation

blending

fusion

and 3D reconstruction. LF has also been innovatively applied to iris and face recognition

identification of materials and fake pedestrians

acquisition of epipolar plane images

shape recovery

and LF microscopy. Here

we further summarize the existing problems and the development trends of LF imaging in computer vision

including the establishment and evaluation of the LF dataset

applications under high dynamic range (HDR) conditions

LF image enhancement

virtual reality

3D display

and 3D movies

military optical camouflage technology

image recognition at micro-scale

image processing method based on HDR

and the optimal relationship between spatial resolution and four-dimensional (4D) LF information acquisition. LF imaging has achieved great success in various studies. Over the past 25 years

more than 180 publications have reported the capability of LF imaging in solving computer vision problems. We summarize these reports to make it easier for researchers to search the detailed methods for specific solutions.

关键词

光场成像相机阵列微透镜阵列极平面图像计算机视觉

Keywords

Light field imagingCamera arrayMicrolens arrayEpipolar plane imageComputer vision

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