A Super-Resolution Imaging Method Based on Dense Subpixel-Accurate Motion Fields

  • Ha V. Le
  • Guna Seetharaman


A super-resolution imaging method suitable for imaging objects moving in a dynamic scene is described. The primary operations are performed over three threads: the computation of a dense inter-frame 2-D motion field induced by the moving objects at a sub-pixel resolution in the first thread. Concurrently, each video image frame is enlarged by the cascode of an ideal low-pass filter and a higher rate sampler, essentially stretching each image onto a larger grid. Then, the main task is to synthesize a higher resolution image from the stretched image of the first frame and that of the subsequent frames subject to a suitable motion compensation. A simple averaging process and/or a simplified Kalman filter may be used to minimize the spatio-temporal noise, in the aggregation process. The method is simple and can take advantage of common MPEG-4 encoding tools. A few experimental cases are presented with a basic description of the key operations performed in the over all process.


Super-resolution motion compensation optical flow 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    R.Y. Tsai and T.S. Huang, “Multiframe Image Restoration and Registration,” in Advances in Computer Vision and Image Processing, R.Y. Tsai and T.S. Huang (Eds.), vol. 1, 1984, JAI Press Inc. pp. 317–339.Google Scholar
  2. 2.
    S.P. Kim and W.-Y. Su, “Recursive High-Resolution Reconstruction of Blurred Multiframe Images,” IEEE Trans. on Image Processing, vol. 2, no. 10, 1993, pp. 534–539.CrossRefGoogle Scholar
  3. 3.
    A.M. Tekalp, M.K. Ozkan, and M.I. Sezan, “High Resolution Image Reconstruction from Low Resolution Image Sequences, and Space Varying Image Restoration,” in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing, San Francisco, CA, vol. 3, 1992, pp. 169–172.Google Scholar
  4. 4.
    M. Elad and Y. Hel-Or, “A Fast Super-Resolution Reconstruction Algorithm for Pure Translational Motion and Common Space-Invariant Blur,” IEEE Trans. on Image Processing, vol. 10, no. 8, 2001, pp. 1187–1193.CrossRefMATHGoogle Scholar
  5. 5.
    M. Irani and S. Peleg, “Motion Analysis for Image Enhancement: Resolution, Occlusion and Transparency,” Journal of Visual Communications and Image Representation, vol. 4, 1993, pp. 324–335.CrossRefGoogle Scholar
  6. 6.
    A.J. Patti, M.I. Sezan, and A.M. Tekalp, “Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Nonzero Aperture Time,” IEEE Trans. on Image Processing, vol. 6, no. 8, 1997, pp. 1064–1076.CrossRefGoogle Scholar
  7. 7.
    M. Elad and A. Feuer, “Restoration of a Single Superesolution Image from Several Blurred, Noisy and Undersampled Measured Images,” IEEE Trans. on Image Processing, vol. 6, no. 12, 1997, pp. 1646–1658.CrossRefGoogle Scholar
  8. 8.
    R.R. Schultz and R.L. Stevenson, “Extraction of High-Resolution Frames from Video Sequences,” IEEE Trans. on Image Processing, vol. 5, no. 6, 1996, pp. 996–1011.CrossRefGoogle Scholar
  9. 9.
    E. Meijering, “A Chronology of Interpolation: From Ancient Astronomy to Modern Signal and Image Processing,” In Proc. of The IEEE, vol. 90, no. 3, 2002, pp. 319–344.CrossRefGoogle Scholar
  10. 10.
    E.C. Cho, S.S. Iyengar, G. Seetharaman, R.J. Holyer, and M. Lybanon, “Velocity Vectors for Features of Sequential Oceanographic Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 3, 1998, pp. 985–998.CrossRefGoogle Scholar
  11. 11.
    Y. Altunbasak and M. Tekalp, “Closed-Form Connectivity-Preserving Solutions for Motion Compensation Using 2-D Meshes,” IEEE Transactions on Image Processing, vol. 6, no. 9, 1997, pp. 1255–1269.CrossRefGoogle Scholar
  12. 12.
    J.F. Canny, “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, 1986, pp. 679–698.CrossRefGoogle Scholar
  13. 13.
    F. Mokhtarian and A.K. Mackworth, “A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 8, 1992, pp. 789–805.CrossRefGoogle Scholar
  14. 14.
    R.N. Strickland and Z. Mao, “Computing Correspondences in a Sequence of Non-Rigid Images,” Pattern Recognition, vol. 25, no. 9, 1992, pp. 901–912.CrossRefGoogle Scholar
  15. 15.
    S. Guha, “An Optimal Mesh Computer Algorithm for Constrained Delaunay Triangulation,” in Proceedings of the International Parallel Processing Symposium, Cancun, Mexico, 1994, pp. 102–109.Google Scholar
  16. 16.
    J.L. Barron, D.J. Fleet, and S.S. Beauchemin, “Performance of Optical Flow Techniques,” International Journal of Computer Vision, vol. 12, no. 1, 1994, pp. 43–77.CrossRefGoogle Scholar
  17. 17.
    M.J. Black and P. Anandan, “The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Field,” Computer Vision and Image Understanding, vol. 63, no. 1, 1996, pp. 75–104.CrossRefGoogle Scholar
  18. 18.
    B.D. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” in Proceedings of the DARPA Image Understanding Workshop, 1981, pp. 121–130.Google Scholar
  19. 19.
    D.J. Fleet and A.D. Jepson, “Computation of Component Image Velocity from Local Phase Information,” International Journal of Computer Vision, vol. 5, no. 1, 1990, pp. 77–104.CrossRefGoogle Scholar
  20. 20.
    P.M. Kuhn, Algorithms, Complexity Analysis and VLSI Architectures for Mpeg-4 Motion Estimation, Kluwer Academic Publishers, Boston, MA, 1999.CrossRefMATHGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc 2006

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringVietnam National UniversityVietnam
  2. 2.Department of Electrical and Computer EngineeringAir Force Institute of Technology

Personalised recommendations