计算摄像学:核心、方法与应用

索津莉; 刘烨斌; 季向阳; 戴琼海

doi:10.16383/j.aas.2015.c130855

计算摄像学:核心、方法与应用

doi: 10.16383/j.aas.2015.c130855 cstr: 32138.14.j.aas.2015.c130855

1.
清华大学清华国家信息实验室, 清华大学自动化系北京 100084

基金项目:

国家自然科学基金(61327902,61120106003,61171119)资助

详细信息

作者简介:
刘烨斌清华大学自动化系副教授.2002年获北京邮电大学学士学位,2009年获清华大学博士学位.主要研究方向为基于图像的建模与渲染,无标记运动捕捉和基于视觉的图形学应用.E-mail:liuyebin@mail.tsinghua.edu.cn

通讯作者:
索津莉清华大学自动化系讲师.2004年获山东大学学士学位,2010年获中国科学院研究生院博士学位.主要研究方向为计算摄像学和计算机视觉.本文通信作者.E-mail:jlsuo@tsinghua.edu.cn

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出版历程
- 收稿日期: 2013-10-16
- 修回日期: 2014-07-18
- 刊出日期: 2015-04-20

Computational Photography: Keys, Methods and Applications

1.
Tsinghua National Laboratory for Information Science and Technology(TNLIST) and Department of Automation, Tsinghua University, Beijing 100084

Funds:

Supported by National Natural Science Foundation of China(61327902, 61120106003, 61171119)

摘要

摘要: 针对现有计算机视觉、图形学、信号处理、数字图像处理、应用光学等领域无法通过现有成像模型与装置及计算方法获取足够目标场景信息的难题,计算摄像学研究提出新的成像机制与对应的计算重构方法,在光信号观测领域另辟蹊径,创新性地将视觉信息处理与计算前移至成像过程,从而极大地提高了信息优化计算的自由度,能够在维度、尺度与分辨率上实现质的突破,从而观测到传统成像系统看不清与看不见的场景信息.本文沿着计算摄像学思路、方法与目标三条主线,对国内外研究现状进行分析与综述,期望能够帮助读者更快地了解及进入相关研究.
- 计算摄像学 /
- 全光函数 /
- 计算光路 /
- 高维高分辨率 /
- 计算重构
Abstract: Current imaging mechanisms and systems cannot capture sufficient visual information of target objects/scenes in many fields, such as computer vision, graphics, signal processing, digital image processing, applied optics, etc. To address these challenges, computational photography has proposed new imaging mechanisms and corresponding reconstruction methods that bring the visual information processing forward to the acquisition process and largely raise the degree of freedom on information optimization. The computational acquisition approaches are able to breakthrough the bottlenecks in dimension, scale, and resolution, and thus can observe the scenes that cannot be captured clearly by traditional imaging systems. This review focuses on three main aspects of computational photographystrategy, approach, and targetand attempts to familiarize the readers with the studies in this field.
- Computational photography /
- plenoptic function /
- computational light path /
- high dimension high resolution /
- computational reconstruction

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参考文献(105)

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计算摄像学:核心、方法与应用

doi: 10.16383/j.aas.2015.c130855 cstr: 32138.14.j.aas.2015.c130855

作者简介:
刘烨斌清华大学自动化系副教授.2002年获北京邮电大学学士学位,2009年获清华大学博士学位.主要研究方向为基于图像的建模与渲染,无标记运动捕捉和基于视觉的图形学应用.E-mail:liuyebin@mail.tsinghua.edu.cn

通讯作者:
索津莉清华大学自动化系讲师.2004年获山东大学学士学位,2010年获中国科学院研究生院博士学位.主要研究方向为计算摄像学和计算机视觉.本文通信作者.E-mail:jlsuo@tsinghua.edu.cn

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Computational Photography: Keys, Methods and Applications

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计算摄像学:核心、方法与应用

doi: 10.16383/j.aas.2015.c130855 cstr: 32138.14.j.aas.2015.c130855

作者简介: 刘烨斌 清华大学自动化系副教授.2002年获北京邮电大学学士学位,2009年获清华大学博士学位.主要研究方向为基于图像的建模与渲染,无标记运动捕捉和基于视觉的图形学应用.E-mail:liuyebin@mail.tsinghua.edu.cn

通讯作者: 索津莉 清华大学自动化系讲师.2004年获山东大学学士学位,2010年获中国科学院研究生院博士学位.主要研究方向为计算摄像学和计算机视觉.本文通信作者.E-mail:jlsuo@tsinghua.edu.cn

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Computational Photography: Keys, Methods and Applications

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作者简介:
刘烨斌清华大学自动化系副教授.2002年获北京邮电大学学士学位,2009年获清华大学博士学位.主要研究方向为基于图像的建模与渲染,无标记运动捕捉和基于视觉的图形学应用.E-mail:liuyebin@mail.tsinghua.edu.cn

通讯作者:
索津莉清华大学自动化系讲师.2004年获山东大学学士学位,2010年获中国科学院研究生院博士学位.主要研究方向为计算摄像学和计算机视觉.本文通信作者.E-mail:jlsuo@tsinghua.edu.cn