Skip to main content
SpringerLink
Log in

An efficient motion magnification system for real-time applications

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

The human eye cannot see subtle motion signals that fall outside human visual limits, due to either limited resolution of intensity variations or lack of sensitivity to lower spatial and temporal frequencies. Yet, these invisible signals can be highly informative when amplified to be observable by a human operator or an automatic machine vision system. Many video magnification techniques have recently been proposed to magnify and reveal these signals in videos and image sequences. Limitations, including noise level, video quality and long execution time, are associated with the existing video magnification techniques. Therefore, there is value in developing a new magnification method where these issues are the main consideration. This study presents a new magnification method that outperforms other magnification techniques in terms of noise removal, video quality at large magnification factor and execution time. The proposed method is compared with four methods, including Eulerian video magnification, phase-based video magnification, Riesz pyramid for fast phase-based video magnification and enhanced Eulerian video magnification. The experimental results demonstrate the superior performance of the proposed magnification method regarding all video quality metrics used. Our method is also 60–70% faster than Eulerian video magnification, whereas other competing methods take longer to execute than Eulerian video magnification.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Poh, M.-Z., Mcduff, D., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Soc. Am. 18(10), 10762–10774 (2010)

    Google Scholar 

  2. Poh, M.-Z., Mcduff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011)

    Article  Google Scholar 

  3. Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16(26), 21434–21445 (2008)

    Article  Google Scholar 

  4. Balakrishnan G., Durand F., Guttag J.: Detecting pulse from head motions in video. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3430–3437 (2013)

  5. Shan, L., Yu, M.: Video-based heart rate measurement using head motion tracking and ICA. In: 2013 6th International Congress on Image and Signal Processing (CISP), pp. 160–164. IEEE (2013)

  6. Irani, R., Nasrollahi, K., Moeslund, T.B.: Improved pulse detection from head motions using DCT. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), pp. 118–124 (2014)

  7. Al-Naji, A., Chahl, J.: Contactless cardiac activity detection based on head motion magnification. Int. J. Image Graph. 17(1), 1–18 (2017)

    Article  Google Scholar 

  8. He, X., Goubran, R.A., Liu, X.P.: Wrist pulse measurement and analysis using Eulerian video magnification. In: 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). pp. 41–44. IEEE (2016)

  9. Al-Naji, A., Chahl, J.: Non-contact heart activity measurement system based on video imaging analysis. Int. J. Pattern Recognit. Artif. Intell. 31(2), 1–21 (2017)

    Article  Google Scholar 

  10. Al-Naji, A., Chahl, J.: Remote respiratory monitoring system based on developing motion magnification technique. Biomed. Signal Process. Control 29, 1–10 (2016)

    Article  Google Scholar 

  11. Ali, A., Kim, G., Sang-Hoen, L., Javaan, C.: Real time apnoea monitoring of children using the Microsoft Kinect sensor: a pilot study. Sensors 17(2), 286 (2017)

    Google Scholar 

  12. Liu, C., Torralba, A., Freeman, W.T., Durand, F., Adelson, E.H.: Motion magnification. ACM Trans. Graph. (TOG) 24(3), 519–526 (2005)

    Article  Google Scholar 

  13. Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J.V., Durand, F., Freeman, W.T.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. (TOG) 31(4), 65 (2012)

    Article  Google Scholar 

  14. Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.T.: Phase-based video motion processing. ACM Trans. Graph. (TOG) 32(4), 80 (2013)

    Article  MATH  Google Scholar 

  15. Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40(1), 49–70 (2000)

    Article  MATH  Google Scholar 

  16. Simoncelli, E.P., Freeman, W.T., Adelson, E.H., Heeger, D.J.: Shiftable multiscale transforms. IEEE Trans. Inf. Theory 38(2), 587–607 (1992)

    Article  MathSciNet  Google Scholar 

  17. Fleet, D.J., Jepson, A.D.: Computation of component image velocity from local phase information. Int. J. Comput. Vis. 5(1), 77–104 (1990)

    Article  Google Scholar 

  18. Gautama, T., Van Hulle, M.M.: A phase-based approach to the estimation of the optical flow field using spatial filtering. IEEE Trans. Neural Netw. 13(5), 1127–1136 (2002)

    Article  Google Scholar 

  19. Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.T.: Riesz pyramid for fast phase-based video magnification. In: 2014 IEEE International Conference on Computational Photography (ICCP), pp. 1–10. IEEE (2014)

  20. Liu, L., Lu, L., Luo, J., Zhang, J., Chen, X.: Enhanced Eulerian video magnification. In: 2014 7th International Congress on Image and Signal Processing (CISP), pp. 50–54. IEEE (2014)

  21. Madhukar, B., Narendra, R.: Lanczos resampling for the digital processing of remotely sensed images. In: Proceedings of International Conference on VLSI, Communication, Advanced Devices, Signals and Systems and Networking (VCASAN-2013), pp. 403–411. Springer (2013)

  22. Kaymaz, E., Lerner, B.T., Campbell, W.J., Le Moigne, J., Pierce, J.F.: Registration of satellite imagery utilizing the low-low components of the wavelet transform. In: 25th Annual AIPR Workshop on Emerging Applications of Computer Vision, International Society for Optics and Photoncs, pp. 45–54 (1997)

  23. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  24. Singh, R., Vasquez, R.E., Singh, R.: Comparison of Daubechies, Coiflet, and Symlet for edge detection. In: Proceedings SPIE 3074, Visual Information Processing VI, pp. 151–159 (1997). https://doi.org/10.1117/12.280616

  25. Dettori, L., Semler, L.: A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography. Comput. Biol. Med. 37(4), 486–498 (2007)

    Article  Google Scholar 

  26. Kale, V.U., Khalsa, N.N.: Performance evaluation of various wavelets for image compression of natural and artificial images. Int. J. Comput. Sci. Commun. 1(1), 179–184 (2010)

    Google Scholar 

  27. Stanković, R.S., Falkowski, B.J.: The Haar wavelet transform: Its status and achievements. Comput. Electr. Eng. 29(1), 25–44 (2003)

    Article  MATH  Google Scholar 

  28. Sandhu, M., Kaur, S., Kaur, J.: A study on design and implementation of Butterworth, Chebyshev and elliptic filter with Matlab. Int. J. Emerg. Technol. Eng. Res. 4(6), 111–114 (2016)

    Google Scholar 

  29. Podder, P., Mehedi Hasan, M., Rafiqul Islam, M., Sayeed, M.: Design and implementation of Butterworth, Chebyshev-I and elliptic filter for speech signal analysis. Int. J. Comput. Appl. 98(7), 12–18 (2014)

    Google Scholar 

  30. Yu, W., Ma, Y., Zheng, L., Liu, K.: Research of improved adaptive median filter algorithm. In: Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation, pp. 27–34. Springer (2016)

  31. Wang, Z., Lu, L., Bovik, A.C.: Video quality assessment based on structural distortion measurement. Sig. Process. Image Commun. 19(2), 121–132 (2004)

    Article  Google Scholar 

  32. Chen, T.S., Chang, C.C., Hwang, M.S.: A virtual image cryptosystem based upon vector quantization. IEEE Trans. Image Process. 7(10), 1485–1488 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  33. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  34. Ruikar, J.D., Sinha, A.K., Chaudhury, S.: Image quality assessment algorithms: study and performance comparison. In: 2014 International Conference on Electronics and Communication Systems (ICECS), pp. 1–4 (2014)

  35. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  36. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, pp. 1398–1402 (2003)

  37. Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 21(4), 1500–1512 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  38. Gondal, I., Murshed, M.: A novel color image fusion QoS measure for multi-sensor night vision applications. In: 2010 IEEE Symposium on Computers and Communications (ISCC), pp. 399–404 (2010)

  39. Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  40. Zhang, L., Li, H.: SR-SIM: a fast and high performance IQA index based on spectral residual. In: 19th IEEE International Conference on Image Processing, pp. 1473–1476 (2012)

  41. Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.: Image quality assessment based on a degradation model. IEEE Trans. Image Process. 9(4), 636–650 (2000)

    Article  Google Scholar 

  42. Peli, E.: Contrast in complex images. J. Opt. Soc. Am. A 7(10), 2032–2040 (1990)

    Article  Google Scholar 

  43. Dodangeh, M., Figeiredo, I.N., Goncalves, G.: Spatially Adaptive Total Variation Deblurring with Split Bregman Technique, pp. 1–24. University of Coimbra, Paço das Escolas (2016)

    Google Scholar 

  44. Mitsa, T., Varkur, K.L.: Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms. In: International Conference on Acoustics, Speech, and Signal Processing, pp. 301–304. IEEE (1993)

  45. Slanina, M., Ricny, V.: A comparison of full-reference image quality assessment methods. In: Radioelektronika 2006 Conference Proceedings. Slovak Technical University in Bratislava, pp. 165–168 (2006). ISBN 80-227-2388-6

  46. Chandler, D.M., Hemami, S.S.: VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. Image Process. 16(9), 2284–2298 (2007)

    Article  MathSciNet  Google Scholar 

  47. Sheikh, H.R., Bovik, A.C., De Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)

    Article  Google Scholar 

  48. Srivastava, A., Lee, A.B., Simoncelli, E.P., Zhu, S.C.: On advances in statistical modeling of natural images. J. Math. Imaging Vis. 18(1), 17–33 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  49. Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)

    Article  Google Scholar 

  50. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  51. Lyshevski, S.E.: Engineering and Scientific Computations Using MATLAB. Wiley, Hoboken (2005)

    Google Scholar 

  52. Abbas, E.I., Noori, A.A.: High resolution direction-of-arrival estimation using genetic algorithm. Eng. Technol. J. 27(9), 1746–1754 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering, University of South Australia, Mawson Lakes, SA, 5095, Australia

    Ali Al-Naji, Sang-Heon Lee & Javaan Chahl

  2. Electrical Engineering Technical College, Middle Technical University, Al Doura, Baghdad, 10022, Iraq

    Ali Al-Naji

  3. Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne, Victoria, 3207, Australia

    Javaan Chahl

Authors
  1. Ali Al-Naji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sang-Heon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javaan Chahl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Al-Naji.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

Ethical approval for the experimental work was given by the UniSA Human Research Ethics Committee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Naji, A., Lee, SH. & Chahl, J. An efficient motion magnification system for real-time applications. Machine Vision and Applications 29, 585–600 (2018). https://doi.org/10.1007/s00138-018-0916-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-018-0916-0

Keywords

Search

Navigation