Theoretical Analysis of Multi-view Camera Arrangement and Light-Field Super-Resolution

  • Ryo Nakashima
  • Keita Takahashi
  • Takeshi Naemura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)


We analyzed a light-field super-resolution problem in which, with a given set of multi-view images with a low resolution, the 3-D scene is reconstructed with a higher resolution using super-resolution (SR) reconstruction. The arrangement of the multi-view cameras is important because it determines the quality of the reconstruction. To simplify the analysis, we considered a situation in which a plane is located at a certain depth and a texture on that plane is super-resolved. We formulated the SR reconstruction process in the frequency domain, where the camera arrangement can be independently expressed as a matrix in the image formation model. We then evaluated the condition number of the matrix to quantify the quality of the SR reconstruction. We clarified that when the cameras are arranged in a regular grid, there exist singular depths in which the SR reconstruction becomes ill-posed. We also determined that this singularity can be avoided if the arrangement is randomly perturbed.


multi-view cameras super-resolution camera arrangement condition number 


  1. 1.
    Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis Machine Intelligence 24(9), 1167–1183 (2002)CrossRefGoogle Scholar
  2. 2.
    Champagnat, F., Besnerais, G.L., Kulcsár, C.: Statistical performance modeling for superresolution: a discrete data-continuous reconstruction framework. Journal of the Optical Society of America A 26(7), 1730–1746 (2009)CrossRefGoogle Scholar
  3. 3.
    Fukushima, N., Ishibashi, Y.: Free viewpoint image generation with super resolution. In: Picture Coding Symposium, pp. 1–4 (2010)Google Scholar
  4. 4.
    Mudenagudi, U., Gupta, A., Goel, L., Kushal, A., Kalra, P., Banerjee, S.: Super Resolution Of Images of 3D Scenecs. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 85–95. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 20(3), 21–36 (2003)CrossRefGoogle Scholar
  6. 6.
    Robinson, D., Milanfar, P.: Statistical performance analysis of super-resolution. IEEE Transactions on Image Processing 15(6), 1413–1428 (2006)CrossRefGoogle Scholar
  7. 7.
    Takahashi, K., Naemura, T., Tanaka, M.: Rate-distortion analysis of super-resolution image/video decoding. In: International Conference on Image Processing (2011)Google Scholar
  8. 8.
    Tanaka, M., Okutomi, M.: Theoretical analysis on reconstruction-based super-resolution for an arbitrary PSF. In: IEEE Computer Vision and Pattern Recognition, vol. 2, pp. 947–954 (2005)Google Scholar
  9. 9.
    Tung, T., Nobuhara, S., Matsuyama, T.: Simultaneous super-resolution and 3d video using graph-cuts. In: IEEE Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ryo Nakashima
    • 1
  • Keita Takahashi
    • 2
  • Takeshi Naemura
    • 1
  1. 1.The University of TokyoBunkyo-kuJapan
  2. 2.The University of Electro-CommunicationsChofu-shiJapan

Personalised recommendations