Advertisement

Image Fusion Algorithm Using the Multiresolution Directional-Oriented Hermite Transform

  • Sonia Cruz-Techica
  • Boris Escalante-Ramirez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

The Hermite transform is introduced as an image representation model for multiresolution image fusion with noise reduction. Image fusion is achieved by combining the steered Hermite coefficients of the source images, then the coefficients are combined with a decision rule based on the linear algebra through a measurement of the linear dependence. The proposed algorithm has been tested on both multi-focus and multi-modal image sets producing results that exceed results achieved with other methods such as wavelets, curvelets [11], and contourlets [2] proving that our scheme best characterized important structures of the images at the same time that the noise was reduced.

Keywords

image fusion Hermite transform multiresolution linear dependence 

References

  1. 1.
    Aguilar-Ponce, R., Tecpanecatl-Xihuitl, J.L., Kumar, A., Bayoumi, M.: Pixel-level image fusion scheme based on linear algebra. In: IEEE International Symposium on Circuits and Systems ISCAS 2007, New Orleans, pp. 2658–2661 (2007)Google Scholar
  2. 2.
  3. 3.
    Durucan, E., Ebrahimi, T.: Change detection and background extraction by linear algebra. Proceedings of the IEEE 89(10), 1368–1381 (2001)CrossRefGoogle Scholar
  4. 4.
    Escalante-Ramírez, B.: The Hermite transform as an efficient model for local image analysis: An application to medical image fusion. Comput. Electr. Eng. 34(2), 99–110 (2008)CrossRefzbMATHGoogle Scholar
  5. 5.
    Escalante-Ramírez, B., López-Caloca, A.: The Hermite transform: an efficient tool for noise reduction and image fusion in remote sensing. In: book: Image Processing for Remote Sensing, pp. 539–557. CRC Press, Boca Raton (2006)Google Scholar
  6. 6.
    Escalante-Ramírez, B., Silván-Cárdenas, J.L.: Advanced modeling of visual information processing: A multi-resolution directional-oriented image transform based on Gaussian derivatives. Signal Processing: Image Communication 20(9-10), 801–812 (2005)Google Scholar
  7. 7.
    Hill, P., Canagarajah, N., Bull, D.: Image Fusion using Complex Wavelets. In: Proc. 13th British Machine Vision Conference, pp. 487–496 (2002)Google Scholar
  8. 8.
    Mahyari, A., Yazdi, M.: A novel image fusion method using curvelet transform based on linear dependency test. In: International Conference on Digital Image Processing, pp. 351–354 (2009)Google Scholar
  9. 9.
    Martens, J.-B.: The Hermite transform-theory. IEEE Transactions on Acoustics, Speech and Signal Processing 38(9), 1595–1606 (1990)CrossRefzbMATHGoogle Scholar
  10. 10.
    Martens, J.-B.: Local orientation analysis in images by means of the Hermite transform. IEEE Transactions on Image Processing 6(8), 1103–1116 (1997)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Van Dijk, A., Martens, J.-B.: Image representation and compression with steered Hermite transforms. Signal Processing 56(1), 1–16 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13, 600–612 (2004)CrossRefGoogle Scholar
  14. 14.
    Wang, Q., Yu, D., Shen, Y.: An overview of image fusion metrics. In: Conference on Instrumentation and Measurement Technology, pp. 918–923 (2009)Google Scholar
  15. 15.
    Yang, L., Guo, B.L., Ni, W.: Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing 72(1-3), 203–211 (2008)CrossRefGoogle Scholar
  16. 16.
    Young, R.: The Gaussian derivative theory of spatial vision: analysis of cortical cell receptive field line-weighting profiles. Integration. Technical report, General Motors Research (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sonia Cruz-Techica
    • 1
  • Boris Escalante-Ramirez
    • 1
  1. 1.Facultad de IngenieríaUniversidad Nacional Autónoma de MéxicoMéxico

Personalised recommendations