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Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition

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Abstract

Image fusion has been receiving increasing attention in the research community with the aim of investigating general formal solutions to a wide spectrum of applications. The objective of this work is to formulate a method that can efficiently fuse multifocus as well as multispectral images for context enhancement and thus can be used by different applications. We propose a novel pixel fusion rule based on multiresolution decomposition of the source images using wavelet, wavelet-packet, and contourlet transform. To compute fused pixel value, we take weighted average of the source pixels, where the weight to be given to the pixel is adaptively decided based on the significance of the pixel, which in turn is decided by the corresponding children pixels in the finer resolution bands. The fusion performance has been extensively tested on different types of images viz. multifocus images, medical images (CT and MRI), as well as IR − visible surveillance images. Several pairs of images were fused to compare the results quantitatively as well as qualitatively with various recently published methods. The analysis shows that for all the image types into consideration, the proposed method increases the quality of the fused image significantly, both visually and quantitatively, by preserving all the relevant information. The major achievement is average 50% reduction in artifacts.

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References

  1. Blum R., Liu Z.: Multi-Sensor Image Fusion and Its Applications. CRC Press, London, United Kingdom (2005)

    Google Scholar 

  2. Wald L.: Data Fusion Definitions and Architectures Fusion of Images of Different Spatial Resolutions. Ecole des Mines de Paris, Paris (2002)

    Google Scholar 

  3. Redondo R., Łroubek F., Fischer S., Cristbal G.: Multifocus image fusion using the log-gabor transform and a multisize windows technique. Elsevier Inf. Fusion 10(2), 163–171 (2009)

    Article  Google Scholar 

  4. Li S., Kwok J.T., Tsang I.W., Wang Y.: Fusing images with different focuses using support vector machines. IEEE Trans. Neural Netw. 15(6), 1555–1561 (2004)

    Article  Google Scholar 

  5. De I., Chanda B.: A simple and efficient algorithm for multifocus image fusion using morphological wavelets. Signal Process. 86(5), 924–936 (2006)

    Article  MATH  Google Scholar 

  6. Zhang Q., Bao-Long G.U.O.: Multifocus image fusion using the nonsubsampled contourlet transform. Elsevier Signal Process. J. 89(7), 1334–1346 (2009)

    MATH  Google Scholar 

  7. Yang, X., Yang, W., Pei, J.: Different focus points images fusion based on wavelet decomposition. In: Proceedings of Third International Conference on Information Fusion, vol. 1, pp. 3–8. (2000)

  8. Arivazhagan S., Ganesan L., Subash Kumar T.G.: A modified statistical approach for image fusion using wavelet transform. Springer J. SIViP 3, 137–144 (2009)

    Google Scholar 

  9. Li S., Yang B.: Multifocus image fusion by combining curvelet and wavelet transform. Pattern Recognit. Lett. 29, 1295–1301 (2008)

    Article  Google Scholar 

  10. Choi M., Kim R.Y., Kim M.G.: The curvelet transform for image fusion. Int. Soc. Photogrammetry Remote Sens. 35(B8), 59–64 (2004)

    Google Scholar 

  11. Sheng Z., Wen-Zhong S., Liu J., Tian J.: Remote sensing image fusion using multiscale mapped LS-SVM. IEEE Trans. Geosci. Remote Sens. 46(5), 1313–1322 (2008)

    Article  Google Scholar 

  12. Guan-qun T.A.O., Da-peng L.I., Guang-hua L.U.: Application of wavelet analysis in medical image fusion. J. Xidian Univ. 31, 82–86 (2004)

    Google Scholar 

  13. Shangli, C., Junmin, H., Zhongwei, L.: Medical image of PET/CT weighted fusion based on wavelet transform. In: International Conference on Bioinformatics and Biomedical Engineering (ICBBE) 2008, pp. 2523–2525, Shanghai, 16–18 may 2008. doi:10.1109/ICBBE.2008.964

  14. Yang, L., Xin, L., Yucui, Y.: Medical image fusion based on wavelet packet transform and self-adaptive operator. In: 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE), pp. 2647–2650. (2008)

  15. Shah P., Merchant S.N., Desai U.B.: Fusion of surveillance images in infrared and visible band using curvelet, wavelet and wavelet packet transform. Int. J. Wavelets Multiresolution Inf. Process. (IJWMIP) 8(2), 271–292 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Ibrahim, S., Wirth, M.: Visible and IR data fusion technique using the contourlet transform. In: International Conference on Computational Science and Engineering, vol. 2, pp. 42–47 (2009)

  17. Chen, H.G., Liu, Y.-Y., Wang, Y.-J.: A novel image fusion method based on wavelet packet transform. In: International Symposium on Knowledge Acquisition and Modeling Workshop, 2008, pp. 462–465, Wuhan, 21–22 Dec 2008. doi:10.1109/KAMW.2008.4810524

  18. Charoentam, O., Patanavijit, V., Jitapunkul, S.: A Stable regionbased multiscale image fusion scheme with thermal and visible image application for mis-registration problem. In: IEEE North-East Workshop on Circuits and Systems, 2006, pp. 113–116, Gatineau, Que, June 2006. doi:10.1109/NEWCAS.2006.250940

  19. Nikolov S., Hill P., Bull D., Canagarajah N.: Wavelets for image fusion. In: Petrosian, A., Meyer, F. (eds) Wavelets in Signal and Image Analysis. Computational Imaging and Vision Series, pp. 213–244. Kluwer Academic Publishers, Dordrecht, The Netherlands (2001)

    Google Scholar 

  20. Wang H., Peng J., Wu W.: Fusion algorithm for multisensor images based on discrete multiwavelet transform. IEEE Proc. Vision Image Signal Process. 149(5), 283–289 (2002)

    Article  Google Scholar 

  21. Li, S., Wang, Y.: Multisensor image fusion using discrete multiwavelet transform. In: Proceedings of the 3rd International Conference on Visual Computing, pp. 93–103 (2000)

  22. Burt, P.J.: A gradient pyramid basis for pattern selective image fusion, Society for Information Display. Digest of Technical Papers, 467–470 (1992)

  23. Li H., Manjunath B.S., Mitra S.K.: Multisensor image fusion using the wavelet transform. Graph. Models Image Process. 57(3), 235–245 (1995)

    Article  Google Scholar 

  24. Pajares G., Cruz J.: A wavelet-based image fusion tutorial. Pattern Recognit. 37(9), 1855–1872 (2004)

    Article  Google Scholar 

  25. Lewis, J.J., OCallaghan, R.J., Nikolov, S.G., Bull, D.R., Canagarajah, C.N.: Region-based image fusion using complex wavelets. In: Proceedings of 7th International Conference on Information Fusion, pp. 555–562 (2004)

  26. Yang, J., Blum, R.S.: Image fusion using the expectation-maximization algorithm and a hidden Markov hodel. In: Vehicular Technology Conference, vol. 6, pp. 4563–4567 (2004). doi:10.1109/VETECF.2004.1404943

  27. Po D.D.-Y., Do M.N.: Directional multiscale modeling of images using the contourlet transform. IEEE Trans. Image Process. 15(6), 1610–1620 (2006)

    Article  MathSciNet  Google Scholar 

  28. Do M.N., Vetterli M.: Contourlets in beyond wavelets. In: Welland, G.V. (ed.) Contourlets, Beyond Wavelets, Academic Press, New York (2003)

    Google Scholar 

  29. Petrovic V., Xydeas C.: Objective image fusion performance characterisation. Proc. ICCV’05 2, 1866–1871 (2005)

    Google Scholar 

  30. Qu G., Zhang D., Yan P.: Information measure for performance of image fusion. Electron. Lett. 38(7), 313–315 (2002)

    Article  Google Scholar 

  31. Toet A., Ijspeert J.K., Waxman A.M., Aguilar M.: Perceptual evaluation of different image fusion schemes. Displays 24, 25–37 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. SPANN Lab, Electrical Engineering Department, IIT Bombay, Mumbai, 400076, India

    Parul Shah & Shabbir N. Merchant

  2. Indian Institute of Technology Hyderabad, Ordnance Factory Estate, Yeddumailaram, Andhra Pradesh, 502205, India

    Uday B. Desai

Authors
  1. Parul Shah
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  2. Shabbir N. Merchant
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  3. Uday B. Desai
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Correspondence to Parul Shah.

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Shah, P., Merchant, S.N. & Desai, U.B. Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition. SIViP 7, 95–109 (2013). https://doi.org/10.1007/s11760-011-0219-7

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  • DOI: https://doi.org/10.1007/s11760-011-0219-7

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