Real Time Human Visual System Based Framework for Image Fusion

  • Gaurav Bhatnagar
  • Q. M. Jonathan Wu
  • Balasubramanian Raman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

Image Fusion is a technique which attempts to combine complimentary information from multiple images of the same scene so that the fused image is more suitable for computer processing tasks and human visual system. In this paper, a simple yet efficient real time image fusion algorithm is proposed considering human visual properties in spatial domain. The algorithm is computationally simple and implemented very easily in real-time applications. Experimental results highlights the expediency and suitability of the algorithm and efficiency is carried by the comparison made between proposed and existing algorithm.

Keywords

Image Fusion Source Image Human Visual System Fusion Algorithm Multi Focus Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Hall, D.L., Llinas, J.: An Introduction to Multisensor Data Fusion. Proceedings of the IEEE 85(1), 6–23 (1997)CrossRefGoogle Scholar
  2. 2.
    Xydeas, C. S., Petrovic, V.: Objective Pixel-level Image Fusion Performance Measure. In: Procedding of SPIE, Sensor Fusion: Architectures, Algorithms, and Applications IV, vol. 4051, pp. 89–98. Society of Photographic Instrumentation Engineers (2002)Google Scholar
  3. 3.
    Chavez, P.S., Kwarteng, A.Y.: Extracting spectral contrast in Landsat thematic mapper image data using selective principal component analysis. Photogrammetric Engineering and Remote Sensing 55, 339–348 (1989)Google Scholar
  4. 4.
    Burt, P.J., Kolczynski, R.J.: Enhanced image capture through fusion. In: Proceedings of International Conference on Computer Vision, Berlin, Germany, pp. 173–182. IEEE Press, Los Alamitos (1993)Google Scholar
  5. 5.
    Li, H., Manjunath, B.S., Mitra, S.K.: Multisensor image fusion using the wavelet transform. Graph Models Image Processing 57(3), 235–245 (1995)CrossRefGoogle Scholar
  6. 6.
    Qu, G., Zhang, D., Yan, P.: Medical Image Fusion by Wavelet Transform Modulus Maxima. Optics Express 9, 184–190 (2001)CrossRefGoogle Scholar
  7. 7.
    Levicky, D., Foris, P.: Human visual system models in digital watermarking. Radioengineering 13(4), 38–43 (2004)Google Scholar
  8. 8.
    Voloshynovskiy, S., Herrigel, A., Baumgartner, N., Pun, T.: A stochastic apporach to content adaptive digital image watermarking. In: Pfitzmann, A. (ed.) IH 1999. LNCS, vol. 1768, pp. 211–236. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gaurav Bhatnagar
    • 1
  • Q. M. Jonathan Wu
    • 1
  • Balasubramanian Raman
    • 2
  1. 1.University of WindsorWindsorCanada
  2. 2.Indian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations