Skip to main content
Springer Nature Link
Log in

Subpixel image registration algorithm based on pyramid phase correlation and upsampling

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

A fast subpixel image registration method is proposed in this paper. The implementation of this method is divided into two steps: coarse registration and fine registration. In the coarse registration stage, we propose a strategy to combine image pyramid with phase correlation; in the fine registration stage, we propose a strategy to perform local upsampling in the frequency domain through matrix multiplication. We compared our algorithm with traditional-feature-based and direct methods, as well as unsupervised learning algorithms. Our empirical results show that compared with traditional methods, our method achieves faster speed, while maintaining equivalent or better accuracy and robustness. In addition, compared with unsupervised learning algorithms, our method can be applied to real-time systems with higher speed requirements, better performance for cases with less overlapping regions, and better robustness to noise.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  2. Bartoli, G.: Image registration techniques: a comprehensive survey. In: Visual Information Processing and Protection Group, pp. 1–54 (2007)

  3. Eastman, R.D., Netanyahu, N.S., Moigne, J.L.: Survey of image registration methods. Image Registr. Remote Sens 21, 35–76 (2011)

  4. Deshmukh, M., Bhosle, U.: A survey of image registration. Int. J. Image Process. (IJIP) 5(3), 245 (2011)

    Google Scholar 

  5. Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP Graph. Models Image Process. 53(3), 231–239 (1991)

    Article  Google Scholar 

  6. Shekarforoush, H., Berthod, M., Zerubia, J., Werman, M.: Sub-pixel Bayesian estimation of albedo and height. Int. J. Comput. Vis. 19(3), 289–300 (1996)

    Article  Google Scholar 

  7. Shekarforoush, H., Chellappa, R.: Data-driven multichannel superresolution with application to video sequences. JOSA A 16(3), 481–492 (1999)

    Article  Google Scholar 

  8. Zhang, Y., Qiao, S., Sun, L., Shi, Q.W., Huang, W., Li, L., Yang, Z.: Photoinduced active terahertz metamaterials with nanostructured vanadium dioxide film deposited by sol–gel method. Opt. Express 22(9), 11070–11078 (2014)

    Article  Google Scholar 

  9. Dong, Y., Long, T., Jiao, W., He, G., Zhang, Z.: A novel image registration method based on phase correlation using low-rank matrix factorization with mixture of Gaussian. IEEE Trans. Geosci. Remote Sens. 56(1), 446–460 (2017)

    Article  Google Scholar 

  10. Wang, C.-W., Ka, S.-M., Chen, A.: Robust image registration of biological microscopic images. Sci. Rep. 4(1), 1–12 (2014)

    Google Scholar 

  11. Lee, C.-Y., Wang, H.-J., Lai, J.-H., Chang, Y.-C., Huang, C.-S.: Automatic marker-free longitudinal infrared image registration by shape context based matching and competitive winner-guided optimal corresponding. Sci. Rep. 7(1), 1–16 (2017)

    Article  Google Scholar 

  12. Guyader, J.-M., Huizinga, W., Poot, D.H.J., van Kranenburg, M., Uitterdijk, A., Niessen, W.J., Klein, S.: Groupwise image registration based on a total correlation dissimilarity measure for quantitative MRI and dynamic imaging data. Sci. Rep. 8(1), 1–14 (2018)

    Article  Google Scholar 

  13. Leprince, S., Barbot, S., Ayoub, F., Avouac, J.-P.: Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Trans. Geosci. Remote Sens. 45(6), 1529–1558 (2007)

    Article  Google Scholar 

  14. Ge, P., Lan, C., Wang, H.: An improvement of image registration based on phase correlation. Optik 125(22), 6709–6712 (2014)

    Article  Google Scholar 

  15. Van den Elsen, P.A., Pol, E.-J.D., Viergever, M.A.: Medical image matching—a review with classification. IEEE Eng. Med. Biol. Mag. 12(1), 26–39 (1993)

    Article  Google Scholar 

  16. Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trends® Comput. Graph. Vis. 2(1), 1–104 (2006)

    MathSciNet  MATH  Google Scholar 

  17. Lucas, B.D, Kanade, T., et al.: An iterative image registration technique with an application to stereo vision. Vancouver, BC (1981)

  18. Evangelidis, G.D., Psarakis, E.Z.: Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1858–1865 (2008)

    Article  Google Scholar 

  19. Lucey, S., Navarathna, R., Ashraf, A.B., Sridharan, S.: Fourier Lucas–Kanade algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1383–1396 (2012)

    Article  Google Scholar 

  20. Yan, Q., Yi, X., Yang, X., Nguyen, T.: Heask: robust homography estimation based on appearance similarity and keypoint correspondences. Pattern Recogn. 47(1), 368–387 (2014)

  21. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  22. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  23. Wu, F., Fang, X.: An improved RANSAC homography algorithm for feature based image mosaic. In: Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry and Artificial Vision, vol. 2 (2007)

  24. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision, pp. 2564–2571. IEEE (2011)

  25. Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: Deepflow: large displacement optical flow with deep matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1385–1392 (2013)

  26. Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: evolution of optical flow estimation with deep networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2462–2470 (2017)

  27. Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T.: Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758–2766 (2015)

  28. Altwaijry, H., Veit, A., Belongie, S.J., Tech, C.: Learning to detect and match keypoints with deep architectures. In: BMVC (2016)

  29. Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. arXiv preprint arXiv:1406.2283 (2014)

  30. DeTone, D., Malisiewicz, T., Rabinovich, A.: Deep image homography estimation. arXiv preprint arXiv:1606.03798 (2016)

  31. Nguyen, T., Chen, S.W., Shivakumar, S.S., Taylor, C.J., Kumar, V.: Unsupervised deep homography: a fast and robust homography estimation model. IEEE Robot. Autom. Lett. 3(3), 2346–2353 (2018)

    Article  Google Scholar 

  32. Tong, X., Ye, Z., Yusheng, X., Gao, S., Xie, H., Qian, D., Liu, S., Xiong, X., Liu, S., Luan, K., et al.: Image registration with Fourier-based image correlation: a comprehensive review of developments and applications. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 12(10), 4062–4081 (2019)

    Article  Google Scholar 

  33. Reed, R.A.: Comparison of subpixel phase correlation methods for image registration. Technical report, Arnold Engineering Development Center Arnold AFS TN (2010)

  34. Alsaade, F.: Fast and accurate template matching algorithm based on image pyramid and sum of absolute difference similarity measure. Res. J. Inf. Technol. 4(4), 204–211 (2012)

    Google Scholar 

  35. Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M.: Pyramid methods in image processing. RCA Eng. 29(6), 33–41 (1984)

    Google Scholar 

  36. Burt, P.J., Adelson, E.H. (eds.): The Laplacian pyramid as a compact image code. In: Readings in Computer Vision, pp. 671–679. Elsevier (1987)

Download references

Author information

Authors and Affiliations

  1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China

    Tianci Li, Jianli Wang & Kainan Yao

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Tianci Li

Authors
  1. Tianci Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jianli Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Kainan Yao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Tianci Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, T., Wang, J. & Yao, K. Subpixel image registration algorithm based on pyramid phase correlation and upsampling. SIViP 16, 1973–1979 (2022). https://doi.org/10.1007/s11760-022-02158-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02158-7

Keywords