Subvoxel Super-Resolution of Volumetric Motion Field Using General Order Prior

  • Koji Kashu
  • Atsushi Imiya
  • Tomoya Sakai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)

Abstract

Super-resolution is a technique to recover a high-resolution image from a low resolution image. We develop a variational super-resolution method for the subvoxel accurate volumetric optical flow computation combining variational super-resolution and the variational optical flow computation for the super-resolution optical flow computation. Furthermore, we use the prior with the fractional order differentiation for the computation of volumetric motion field to control the continuity order of the field. Our method computes the gradient and the spatial difference of a high-resolution images from these of low-resolution images directly, without computing any high resolution images which are used as intermediate data for the computation of optical flow vectors of the high-resolution image.

Keywords

Volumetric Image Image Pyramid Optical Flow Computation Optical Flow Vector Fractional Order Differentiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J.: Highly accurate optic flow computation with theoretically justified warping. IJCV 67, 141–158 (2006)CrossRefGoogle Scholar
  2. 2.
    Davis, J.A., Smith, D.A., McNamara, D.E., Cottrell, D.M., Campos, J.: Fractional derivatives-analysis and experimental implementation. Applied Optics 32, 5943–5948 (2001)CrossRefGoogle Scholar
  3. 3.
    Tseng, C.-C., Pei, S.-C., Hsia, S.-C.: Computation of fractional derivatives using Fourier transform and digital FIR differentiator. Signal Processing 80, 151–159 (2000)CrossRefMATHGoogle Scholar
  4. 4.
    Blu, T., Unser, M.: Image interpolation and resampling. In: Handbook of Medical Imaging, Processing and Analysis, pp. 393–420. Academic Press, London (2000)Google Scholar
  5. 5.
    Stark, H. (ed.): Image Recovery: Theory and Application. Academic Press, New York (1992)Google Scholar
  6. 6.
    Wahba, G., Wendelberger, J.: Some new mathematical methods for variational objective analysis using splines and cross-validation. Monthly Weather Review 108, 36–57 (1980)CrossRefGoogle Scholar
  7. 7.
    Pock, T., Urschler, M., Zach, C., Beichel, R.R., Bischof, H.: A duality based algorithm for TV- L 1-optical-flow image registration. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 511–518. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Marquina, A., Osher, S.J.: Image super-resolution by TV-regularization and Bregman iteration. Journal of Scientific Computing 37, 367–382 (2008)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Chambolle, A.: An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision 20, 89–97 (2004)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Youla, D.: Generalized image restoration by the method of alternating orthogonal projections. IEEE Transactions on Circuits and Systems 25, 694–702 (1978)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)CrossRefGoogle Scholar
  12. 12.
    Burt, P.J., Andelson, E.H.: The Laplacian pyramid as a compact image coding. IEEE Trans. Communications 31, 532–540 (1983)CrossRefGoogle Scholar
  13. 13.
    Hwan, S., Hwang, S.-H., Lee, U.K.: A hierarchical optical flow estimation algorithm based on the interlevel motion smoothness constraint. Pattern Recognition 26, 939–952 (1993)CrossRefGoogle Scholar
  14. 14.
    Shin, Y.-Y., Chang, O.-S., Xu, J.: Convergence of fixed point iteration for deblurring and denoising problem. Applied Mathematics and Computation 189, 1178–1185 (2007)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Beauchemin, S.S., Barron, J.L.: The computation of optical flow. ACM Computer Surveys 26, 433–467 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Koji Kashu
    • 1
  • Atsushi Imiya
    • 2
  • Tomoya Sakai
    • 3
  1. 1.School of Advanced Integration ScienceChiba UniversityJapan
  2. 2.Institute of Media and Information TechnologyChiba UniversityInage-kuJapan
  3. 3.Department of Computer and Information SciencesNagasaki UniversityBunkyo-choJapan

Personalised recommendations