Advertisement

Signal, Image and Video Processing

, Volume 12, Issue 8, pp 1505–1512 | Cite as

A new video magnification technique using complex wavelets with Radon transform application

  • Omar M. FahmyEmail author
  • Gamal Fahmy
  • Mamdouh F. Fahmy
Original Paper
  • 298 Downloads

Abstract

Magnifying micro-movements of natural videos that are undetectable by human eye has recently received considerable interests, due to its impact in numerous applications. In this paper, we use dual tree complex wavelet transform (DT-CWT), to analyze video frames in order to detect and magnify micro-movements to make them visible. We use DT-CWT, due to its excellent edge-preserving and nearly-shift invariant features. In order to detect any minor change in object’s spatial position, the paper proposes to modify the phases of the CWT coefficients decomposition of successive video frames. Furthermore, the paper applies Radon transform to track frame micro-movements without any temporal band-pass filtering. The paper starts by presenting a simple technique to design orthogonal filters that construct this CWT system. Next, it is shown that modifying the phase differences between the CWT coefficients of arbitrary frame and a reference one results in image spatial magnification. This in turn, makes these micro-movements seen and observable. Several simulation results are given, to show that the proposed technique competes very well to the existing micro-magnification approaches. In fact, as it manages to yield superior video quality in far less computation time.

Keywords

Complex wavelets Phase video magnification Radon transform 

Notes

Acknowledgements

Funding was provided by Deanship of Scientific Research, Prince Sattam Bin abdul Aziz University (Grant No. Project 2017/01/7140).

Supplementary material

11760_2018_1306_MOESM1_ESM.avi (19.9 mb)
Supplementary material 1 (avi 20349 KB)
11760_2018_1306_MOESM2_ESM.avi (28.4 mb)
Supplementary material 2 (avi 29122 KB)

Supplementary material 3 (avi 27495 KB)

References

  1. 1.
    Wang, J., Drucker, S.M., Agrawala, M., Cohen, M.F.: The cartoon animation filter. ACM Trans. Graph. 25, 1169–1173 (2006)CrossRefGoogle Scholar
  2. 2.
    Poh, M.Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18(10), 10762–10774 (2010)CrossRefGoogle Scholar
  3. 3.
    Fuchs, M., Chen, T., Wang, O., Raskar, R., Seidel, H.P., Lensch, H.P.: Real-time temporal shaping of high-speed video streams. Comput. Graph, 34(5), 575–584 (2010)CrossRefGoogle Scholar
  4. 4.
    Liu, C., Torralba, A., Freeman, W.T., Durand, F., Adelson, E.H.: Motion magnification. ACM Trans. Graph. 24(3), 519–526 (2005)CrossRefGoogle Scholar
  5. 5.
    Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4), 1 (2012)CrossRefGoogle Scholar
  6. 6.
    Wadhwal, N., Rubinstein, M., Durand, F., Freeman, W.T.: Phase-based video motion processing. ACM Trans. Graph. 32(4), 80 (2013)Google Scholar
  7. 7.
    Simoncelli, P.E., Freeman, W.T.: The steerable pyramid: a flexible architecture for multi-scae derivative computation. IEEE Int. Conf. Image Proces. 3, 444–447 (1995)CrossRefGoogle Scholar
  8. 8.
    Freeman, W.T., Adelson, E.H., et al.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)CrossRefGoogle Scholar
  9. 9.
    Fahmy, G., Fahmy, O.M., Fahmy, M.F.: Micro movement magnification in video signals using complex wavelet analysis. IET Image Process. 11(11), 986–993 (2017)CrossRefGoogle Scholar
  10. 10.
    Kingsbury, N.: A dual-tree complex wavelet transform with improved orthogonality and symmetry properties. IEEE Int. Conf. Image Proces. 2, 375–378 (2000)Google Scholar
  11. 11.
    Kingsbury, N.G.: Complex wavelets for shift invariant analysis and filtering of signals. Appl. Comput. Harmonic Anal. 10(3), 234–253 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Selesnick, I.W., Baraniuk, R., Kingsbury, N.G.: The dual-tree complex wavelet transform. IEEE Signal Process. Mag. 22(6), 123–151 (2005)CrossRefGoogle Scholar
  13. 13.
    Smith, M.J., Docef, A.: A Study Guide for Digital Image Processing. Scientific Publishers, Jodhpur (1997)Google Scholar
  14. 14.
    Kingston, A.: Orthogonal discrete Radon transform over pn \(\times \) PN images. Signal Process. 86(8), 2040–2050 (2006)CrossRefzbMATHGoogle Scholar
  15. 15.
    Fahmy, O.M., Fahmy, M.F.: An enhanced denoising technique using dual tree complex wavelet transform. In: National Radio Science Conference NRSC, AASRT, Aswan, Egypt (2016)Google Scholar
  16. 16.
    Fahmy, O.M., Fahmy, M.F.: An efficient bivariate image denoising technique using new orthogonal CWT filters. IET Image Process. J. 10(1049), 1117 (2018)Google Scholar
  17. 17.
    Vaidyanathan, P.: Multirate Systems and Filter Banks. Pearson Education India, London (1993)zbMATHGoogle Scholar
  18. 18.
    Abdelnour, A.F., Selesnick, I.W.: Nearly symmetric orthogonal wavelet bases. In: IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP) (2001 May)Google Scholar
  19. 19.
    Kingsbury, N.G.: Complex wavelets for shift invariant analysis and filtering of signals. J. Appl. Comput. Harmonic Anal. 10(3), 234–253 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Miller, M.A., Kingsbury, N.G.: Image modeling using inter scale phase properties of complex wavelet coefficients. IEEE Trans. Image Process. 17(9), 1491–99 (2008)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentFuture University in Egypt (FUE)CairoEgypt
  2. 2.Electrical Engineering DepartmentPrince Sattam Bin Abdulaziz UniversityAl-SaihSaudi Arabia
  3. 3.Electrical Engineering DepartmentAssiut University in EgyptAsyûtEgypt

Personalised recommendations