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Infrared and Visible Image Fusion Using Entropy and Neuro-Fuzzy Concepts

  • S. Rajkumar
  • P. V. S. S. R. Chandra Mouli
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)

Abstract

Image fusion is the process to derive the useful information from the scene captured by infrared (IR) and visible images. This derived information is used to improve the image content by enhancing the image visualization. Human identification or any living object identification in IR images is a challenging task. This paper proposes two fusion techniques namely Discrete Wavelet Transform with Neuro-Fuzzy (NF) and Entropy (EN) (DWT-NF-EN) and Integer Wavelet Transform with Neuro-Fuzzy and Entropy (IWT-NF-EN) and their results are compared and analyzed with existing fusion techniques using different quantitative measures. Subjective and objective evaluation of the results obtained is compared with other fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Integer Wavelet Transform and Neuro-Fuzzy (IWT-NF). The objective evaluation is done using the quantitative measures Entropy (EN), Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). From the experimental results it is observed that proposed methods provided better information (quality) using EN, PSNR and NCC measures for majority of the test images and the same is justified with the subjective results.

Keywords

Infrared and visible images Integer Wavelet Transform Discrete Wavelet Transform Neuro-Fuzzy RDWT Fusion 

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References

  1. 1.
    Toet, A., van Ruyven, J.J., Valeton, J.M.: Merging thermal and visual images by a contrast pyramid. Optical Engineering 28, 789–792 (1989)CrossRefGoogle Scholar
  2. 2.
    Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Transactions on Communications 31, 532–540 (1983)CrossRefGoogle Scholar
  3. 3.
    Toet, A.: Image fusion by a ratio of low-pass pyramid. Pattern Recognition Letters 9, 245–253 (1989)CrossRefMATHGoogle Scholar
  4. 4.
    Toet, A.: A morphological pyramidal image decomposition. Pattern Recognition Letters 9, 255–261 (1989)CrossRefMATHGoogle Scholar
  5. 5.
    Li, M., Wu, S.: A New Image Fusion Algorithm Based on Wavelet Transform. In: Proceedings of International Conference on Computational Intelligence and Multimedia Applications, pp. 154–159 (2003)Google Scholar
  6. 6.
    Yang, L., Guo, B.L., Ni, W.: Multimodality Medical Image Fusion Based on Multiscale Geometric Analysis of Contourlet Transform. Neuro Computing 72, 203–211 (2008)Google Scholar
  7. 7.
    Filippo, N., Andrea, G., Stefano, B.: Remote Sensing Image Fusion Using the Curvelet Transform. Information Fusion 8, 143–156 (2007)CrossRefGoogle Scholar
  8. 8.
    Singh, R., Vastsa, M., Noore, A.: Multimodal Medical Image Fusion using Redundant Discrete Wavelet Transform. In: Seventh International Conference on Advances in Pattern Recognition, pp. 232–235 (2009)Google Scholar
  9. 9.
    Wang, Z., Yu, X., Zhang, L.B.: A Remote Sensing Image Fusion Algorithm Based on Integer Wavelet Transform. Journal of Optoelectronics Laser 19, 1542–1545 (2008)Google Scholar
  10. 10.
    Rajkumar, S., Kavitha, S.: Redundancy Discrete Wavelet Transform and Contourlet Transform for Multimodality Medical Image Fusion with Quantitative Analysis. In: Third International Conference on Emerging Trends in Engineering and Technology, pp. 134–139 (2010)Google Scholar
  11. 11.
    Kavitha, C.T., Chellamuthu, C.: Multimodal Medical Image Fusion Based on Integer Wavelet Transform and Neuro-Fuzzy. In: International Conference on Signal and Image Processing, pp. 296–300 (2010)Google Scholar
  12. 12.
    Prakash, C., Rajkumar, S., Chandra Mouli, P.V.S.S.R.: Medical Image Fusion based on Redundancy DWT and Mamdani type min sum mean-of-max techniques with Quantitative Analysis. In: International Conference on Recent Advances in Computing and Software Systems, pp. 54–59 (2012)Google Scholar
  13. 13.
    Saeedi, J., Faez, K.: Infrared and visible image fusion using fuzzy logic and population-based optimization. Applied Soft Computing 12, 1041–1054 (2011)CrossRefGoogle Scholar
  14. 14.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall (2007)Google Scholar
  15. 15.
    Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • S. Rajkumar
    • 1
  • P. V. S. S. R. Chandra Mouli
    • 1
  1. 1.School of Computing Science and EngineeringVIT UniversityVelloreIndia

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