Skip to main content

Phase Correlation Based Image Alignment with Subpixel Accuracy

  • Conference paper
Advances in Artificial Intelligence (MICAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7629))

Included in the following conference series:


The phase correlation method is a well-known image alignment technique with broad applications in medical image processing, image stitching, and computer vision. This method relies on estimating the maximum of the phase-only correlation (POC) function, which is defined as the inverse Fourier transform of the normalized cross-spectrum between two images. The coordinates of the maximum correspond to the translation between the two images. One of the main drawbacks of this method, in its basic form, is that the location of the maximum can only be obtained with integer accuracy. In this paper, we propose a new technique to estimate the location with subpixel accuracy, by minimizing the magnitude of gradient of the POC function around a point near the maximum. We also present some experimental results where the proposed method shows an increased accuracy of at least one order of magnitude with respect to the base method. Finally, we illustrate the application of the proposed algorithm to the rigid registration of digital images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Kuglin, C.D., Hines, D.C.: The Phase Correlation Image Alignment Method. In: Proc. of the IEEE Int. Conf. on Cybernetics and Society, pp. 163–165 (1975)

    Google Scholar 

  2. De Castro, E., Morandi, C.: Registration of Translated and Rotated Images Using Finite Fourier Transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence 9, 700–703 (1987)

    Article  Google Scholar 

  3. Reddy, B.S., Chatterji, B.N.: An FFT-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration. IEEE Transactions on Image Processing 5, 1266–1271 (1996)

    Article  Google Scholar 

  4. Keller, Y., Averbuch, A., Moshe, I.: Pseudopolar-based estimation of large translations, rotations, and scalings in images. IEEE Transactions on Image Processing 14, 12–22 (2005)

    Article  MathSciNet  Google Scholar 

  5. Keller, Y., Shkolnisky, Y., Averbuch, A.: The angular difference function and its application to image registration. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 969–976 (2005)

    Article  Google Scholar 

  6. Huang, J.Z., Tan, T.N., Ma, L., Wang, Y.H.: Phase correlation based iris image registration model. J. Comput. Sci. & Technol. 20, 419–425 (2005)

    Article  Google Scholar 

  7. Ito, K., Morita, A., Aoki, T., Higuchi, T., Nakajima, H., Kobayashi, K.: A fingerprint recognition algorithm using phase-based image matching for low-quality fingerprints. In: IEEE International Conference on Image Processing (ICIP 2005), vol. 2, pp. 33–36 (2005)

    Google Scholar 

  8. Kolar, R., Sikula, V., Base, M.: Retinal image registration using phase correlation. In: Analysis of Biomedical Signals and Images (Proceedings of the 20th International Eurasip Conference), vol. 20, pp. 244–252 (2010)

    Google Scholar 

  9. Muquit, M.A., Shibahara, T., Aoki, T.: A High-Accuracy Passive 3D Measurement System Using Phase-Based Image Matching. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E89-A, 686–697 (2006)

    Article  Google Scholar 

  10. Alba, A., Arce-Santana, E., Aguilar Ponce, R.M., Campos-Delgado, D.U.: Phase-correlation guided area matching for realtime vision and video encoding. Journal of Real-Time Image Processing (in press, 2012)

    Google Scholar 

  11. Takita, K., Muquit, M.A., Aoki, T., Higuchi, T.: A Sub-Pixel Correspondence Search Technique for Computer Vision Applications. IECIE Trans. Fundamentals E87-A, 1913–1923 (2004)

    Google Scholar 

  12. Chien, L.H., Aoki, T.: Robust Motion Estimation for Video Sequences Based on Phase-Only Correlation. In: 6th IASTED International Conference on Signal and Image Processing, pp. 441–446 (2004)

    Google Scholar 

  13. Abdou, I.E.: Practical approach to the registration of multiple frames of video images. In: Proc. SPIE, Visual Communications and Image Processing 1999, vol. 3653, pp. 371–382 (1998)

    Google Scholar 

  14. Foroosh, H., Zerubia, J.B., Berthod, M.: Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing 11, 188–200 (2002)

    Article  Google Scholar 

  15. Takita, K., Aoki, T., Sasaki, Y., Higuchi, T., Kobayashi, K.: High-accuracy subpixel image registration based on phase-only correlation. IEICE Trans. Fundamentals E86-A, 1925–1934 (2003)

    Google Scholar 

  16. Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)

    Article  MATH  Google Scholar 

  17. Hoge, W.S.: A subspace identification extension to the phase correlation method. IEEE Transactions on Medical Imaging 22, 277–280 (2003)

    Article  Google Scholar 

  18. Arce-Santana, E., Alba, A.: Image Registration Using Markov Random Coefficient Fields. In: Brimkov, V.E., Barneva, R.P., Hauptman, H.A. (eds.) IWCIA 2008. LNCS, vol. 4958, pp. 306–317. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Alba, A., Aguilar-Ponce, R.M., Vigueras-Gómez, J.F., Arce-Santana, E. (2013). Phase Correlation Based Image Alignment with Subpixel Accuracy. In: Batyrshin, I., González Mendoza, M. (eds) Advances in Artificial Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7629. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37806-5

  • Online ISBN: 978-3-642-37807-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics