Iterative Gradient-Based Shift Estimation: To Multiscale or Not to Multiscale?

  • Martin RaisEmail author
  • Jean-Michel Morel
  • Gabriele Facciolo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)


Fast global shift estimation is a critical preprocessing step on many high level tasks such as remote sensing or medical imaging. In this work we deal with a simple question: should we use an iterative technique to perform shift estimation or should we use a multiscale approach. Based on the obtained results, both methodologies proved to lose accuracy as the noise increases, however this accuracy loss increases with the shift magnitude. The conclusion is that a multiscale strategy should be used when the shift magnitude is higher than approximately a fifth of a pixel.


Shift estimation Multiscale Iterative 


  1. 1.
    Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. Int. J. Comput. Vision 56(3), 221–255 (2004)CrossRefGoogle Scholar
  2. 2.
    Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Transactions on Communications 31(4), 532–540 (1983)CrossRefGoogle Scholar
  3. 3.
    Foroosh, H., Zerubia, J., Berthod, M.: Extension of phase correlation to subpixel registration. IEEE TIP 11(3), 188–200 (2002)Google Scholar
  4. 4.
    Goshtasby, A., Stockman, G., Page, C.: A Region-Based Approach to Digital Image Registration with Subpixel Accuracy. IEEE Transactions on Geoscience and Remote Sensing GE-24(3) (1986)Google Scholar
  5. 5.
    Guizar-Sicairos, M., Thurman, S.T., Fienup, J.R.: Efficient subpixel image registration algorithms. Opt. Lett. 33(2), 156–158 (2008)CrossRefGoogle Scholar
  6. 6.
    Horn, B.K., Schunck, B.G.: Determining optical flow (1981)Google Scholar
  7. 7.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, vol. 2, pp. 674–679 (1981)Google Scholar
  8. 8.
    Morel, J.M., Yu, G.: Is sift scale invariant? Inverse Problems and Imaging 5(1), 115–136 (2011)CrossRefzbMATHGoogle Scholar
  9. 9.
    Pham, T.Q., Bezuijen, M., Van Vliet, L.J., Schutte, K., Luengo Hendriks, C.L.: Performance of optimal registration estimators. In: Proc. SPIE, vol. 5817, pp. 133–144 (2005)Google Scholar
  10. 10.
    Pham, T., Duggan, M.: Bidirectinal bias correction for gradient-based shift estimation. In: IEEE ICIP, pp. 829–832, Oct 2008Google Scholar
  11. 11.
    Rais, M., Thiebaut, C., Delvit, J.M., Morel, J.M.: A tight multiframe registration problem with application to earth observation satellite design. In: 2014 IEEE International Conference on Imaging Systems and Techniques, pp. 6–10 (2014)Google Scholar
  12. 12.
    Reddy, B., Chatterji, B.: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE TIP 5(8), 1266–1271 (1996)Google Scholar
  13. 13.
    Robinson, D., Milanfar, P.: Fundamental performance limits in image registration. IEEE TIP 13(9), 1185–1199 (2004)Google Scholar
  14. 14.
    Robinson, D., Milanfar, P.: Bias minimizing filter design for gradient-based image registration. Signal Processing: Image Communication 20(6), 554–568 (2005). Special Issue on Advanced Aspects of Motion EstimationGoogle Scholar
  15. 15.
    Sabater, N., Leprince, S., Avouac, J.P.: Contrast invariant and affine sub-pixel optical flow. In: IEEE ICIP, pp. 53–56 (2012)Google Scholar
  16. 16.
    Thévenaz, P., Ruttimann, U.E., Unser, M.: A pyramid approach to subpixel registration based on intensity. IEEE TIP 7(1), 27–41 (1998)Google Scholar
  17. 17.
    Tian, Q., Huhns, M.N.: Algorithms for subpixel registration. Comput. Vision Graph. Image Process. 35(2), 220–233 (1986)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Martin Rais
    • 1
    • 2
    Email author
  • Jean-Michel Morel
    • 2
  • Gabriele Facciolo
    • 2
  1. 1.DMI, UIBPalma, MajorcaSpain
  2. 2.CMLA, ENS-CachanCachanFrance

Personalised recommendations