Comparative Analysis of Image Fusion Performance Evaluation Methods for the Real-Time Environment Monitoring System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 184)

Summary

We discuss and compare several objective measures used for image fusion algorithm performance evaluation. Subjective assessments are given as well. Many of the considered evaluation methods originate from prior literature, we also introduce measure based on Jensen-Shannon divergence and a simple gradient-based measure, particularly well fitted for the real time fusion evaluation issue. Along with several well known fusion methods we put under tests recently developed, promising algorithm based on the fast and adaptive bidimensional empirical mode decomposition.

Keywords

Image Fusion Empirical Mode Decomposition Shannon Entropy Fusion Algorithm Fusion Result 
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.

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References

  1. 1.
    Putz, B., Timofiejczuk, A., Bartys, M., Gwardecki, J.: System of TV and thermal image fusion for real-time application monitoring. Pomiary Automatyka Kontrola 57(7), 784–788 (2011)Google Scholar
  2. 2.
    Putz, B., Bartyś, M., Antoniewicz, A., Klimaszewski, J., Kondej, M., Wielgus, M.: Real-time Image Fusion Monitoring System: Problems and Solutions. In: Choraś, R.S. (ed.) Image Processing & Communications Challenges 4. AISC, vol. 184, pp. 147–156. Springer, Heidelberg (2012)Google Scholar
  3. 3.
    Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters 9(3), 81–84 (2002)CrossRefGoogle Scholar
  4. 4.
    Piella, G.: New quality measures for image fusion. In: Proc. 7th Int. Conf. on Information Fusion, pp. 542–546 (2004)Google Scholar
  5. 5.
    Mitchell, H.B.: Image Fusion: Theories, Techniques and Applications. Springer (2010)Google Scholar
  6. 6.
    Rockinger, O.: Image sequence fusion using a shift invariant wavelet transform. In: Proc. IEEE Int. Conf. Image Processing, vol. 13, pp. 288–291 (1997)Google Scholar
  7. 7.
    Ahmed, M.U., Mandic, D.P.: Image fusion based on Fast and Adaptive Bidimensional Empirical Mode Decomposition. In: Proc. IEEE 13th Int. Conf. on Information Fusion, pp. 1–6 (2010)Google Scholar
  8. 8.
    Wielgus, M., Antoniewicz, A., Bartys, M., Putz, B.: Fast and Adaptive Bidimensional Empirical Mode Decomposition for the Real-time Video Fusion. Accepted at IEEE 15th Int. Conf. on Information Fusion, pp. 1–6 (2012)Google Scholar
  9. 9.
    Bhuiyan, S.M., Adhami, R.R., Khan, J.F.: A novel approach of fast and adaptive bidimensional empirical mode decomposition. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 1313–1316 (2008)Google Scholar
  10. 10.
    Zhang, X., Chen, Q., Men, T.: Comparison of fusion methods for the infrared and color visible images. In: Proc. 2nd IEEE Int. Conf. on Computer Science and Information Technology, pp. 421–424 (2009)Google Scholar
  11. 11.
    Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics Letters 38, 313–315 (2002)CrossRefGoogle Scholar
  12. 12.
    Cvejic, N., Canagarajah, C.N., Bull, D.R.: Image fusion metric based on mutual information and Tsallis entropy. Electronics Letters 42, 626–627 (2006)CrossRefGoogle Scholar
  13. 13.
    Zheng, Y., Qin, Z., Shao, L., Hou, X.: A Novel Objective Image Quality Metric for Image Fusion Based on Renyi Entropy. Information Technology Journal 7, 930–935 (2008)CrossRefGoogle Scholar
  14. 14.
    Xydeas, C., Petrovic, V.: Objective image fusion performance measure. Electronics Letters 36, 308–309 (2000)CrossRefGoogle Scholar
  15. 15.
    Jamrozik, W., Fidali, M.: Estimation of image fusion methods for purposes of vision monitoring of industrial process. In: Proc. of Diagnostics of Processes and Systems, pp. 459–464 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Micromechanics and PhotonicsWarsawPoland
  2. 2.Institute of Automatic Control and RoboticsWarsawPoland

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