吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 1-5.

• 论文 • 上一篇    下一篇

组合成像中的立体元阵列合成与稀疏视点采集

吕源治1, 王世刚1, 张丹彤2   

  1. 1. 吉林大学 通信工程学院, 长春130012;
    2. 吉林大学 材料科学与工程学院, 长春 130012
  • 收稿日期:2012-05-17 出版日期:2013-06-01 发布日期:2013-06-01
  • 作者简介:吕源治(1986-),男,博士研究生.研究方向:立体视频.E-mail:lyz123489@163.com
  • 基金资助:

    国家自然科学基金重点项目(U0935001);吉林省科技发展计划基础研究项目(20090506);吉林省科技发展计划项目(20100306).

Elemental image array generation and sparse viewpoint pickup in integral imaging

LYU Yuan-zhi1, WANG Shi-gang1, ZHANG Dan-tong2   

  1. 1. College of Communication Engineering, Jilin University, Changchun 130012, China;
    2. College of Materials Science and Engineering, Jilin University, Changchun 130012, China
  • Received:2012-05-17 Online:2013-06-01 Published:2013-06-01

摘要:

针对组合成像系统中的立体元图像阵列由于采集设备等因素限制而难以实景拍摄的问题,提出了一种利用稀疏视点图像通过并行映射获得立体元图像阵列的方法。该方法首先使用相机阵列拍摄实际景物的稀疏视点图像,然后分别计算每幅图像的水平和垂直视差图并重构出图像中每个像素所对应实际物点的空间位置,最后采用并行映射的方法生成立体元图像阵列,对于立体元图像中仍然存在的空洞,采用插值计算的方法进行填补。实验结果给出了采集到的稀疏视点图像以及合成后的立体元图像阵列,结果表明,合成图像具有连续的视差变化,可以真实再现拍摄对象的空间结构,而且本文方法在实现上优于传统的立体元图像阵列采集方法。

关键词: 信息处理技术, 组合成像, 稀疏视点采集, 并行映射

Abstract:

To solve the location shooting problem of the elemental image array in integral imaging which is as the result of the pickup device limitations,a novel method to generate elemental image array from the sparse viewpoint images by parallel mapping was proposed.First,a camera array was used to capture the sparse viewpoint images of the objects.Then,the horizontal and vertical disparity maps of every image were calculated,respectively,and the spatial location of the actual object point corresponding to every pixel in the image was reconstructed.Finally,the elemental image array was generated by using the parallel mapping algorithm.For the holes which still exist in the elemental image,the interpolation algorithm was uesd to fill them.The captured sparse viewpoint images and the synthesized elemental image array were presented in the experimental results.The experiment results show that the synthesized image has continuous visual angles and can truly represent the space structure of the subjects.And the proposed method is superior to the traditional elemental image array pickup method in the implementation.

Key words: information processing, integral imaging, sparse viewpoint pickup, parallel mapping

中图分类号: 

  • TN911.73

[1] Lippmann M G.Epreuves reversible donnant la sensation du relief[J].Journal of Physiology,1908,1(7):821-825.

[2] Ives H E.Optical properties of a lippmann lenticulated sheet[J].Journal of the Optical Society of America,1931,21(3):171-176.

[3] Davies N,McCormick M,Brewin M.Design and analysis of an image transfer system using microlens arrays[J].Optical Engineering,1994,33(11):3624-3633.

[4] Hwang Y S,Hong S-H,Javidi B.Free view 3-D visualization of occluded objects by using computational synthetic aperture integral imaging[J].J. Display Technol.,2007,3(1):64-70.

[5] Park J-H, Hong K, Lee B.Recent progress in three-dimensional information processing based on integral imaging[J].Applied Optics,2009,48(34):H77-H94.

[6] Park J-H,Min S-W, Jung S,et al.Analysis of viewing parameters for two display methods based on integral photography[J].Applied Optics,2001,40(29):5217-5232.

[7] Liao H,Dohi T, Nomura K.Autostereoscopic 3D display with long visualization depth using referential viewing area-based integral photography[J].IEEE Transactions on Visualization and Computer Graphics,2011,17(11):1690-1701.

[8] Okano F, Hoshino H, Arai J,et al.Realtime pickup method for a three-dimensional image based on integral photography[J].Applied Optics,1997,36(7):1598-1603.

[9] Jang J-S,Javidi B.Improved viewing resolution of three-dimensional integral imaging by use of nonstationary micro-optics[J].Optics Letters,2002,27(5):324-326.

[10] Jang J-S,Javidi B.Three-dimensional synthetic aperture integral imaging[J].Optics Letters,2002,27(13):1144-1146.

[11] Levoy M.Light fields and computational imaging[J].IEEE Computer Magazine,2006,39(8):46-55.

[12] Park J-H, Kim J, Lee B.Three-dimensional optical correlator using a sub-image array[J].Optics Express,2005,13(13):5116-5126.

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