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.
Similar content being viewed by others
References
Blum R., Liu Z.: Multi-Sensor Image Fusion and Its Applications. CRC Press, London, United Kingdom (2005)
Wald L.: Data Fusion Definitions and Architectures Fusion of Images of Different Spatial Resolutions. Ecole des Mines de Paris, Paris (2002)
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)
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)
De I., Chanda B.: A simple and efficient algorithm for multifocus image fusion using morphological wavelets. Signal Process. 86(5), 924–936 (2006)
Zhang Q., Bao-Long G.U.O.: Multifocus image fusion using the nonsubsampled contourlet transform. Elsevier Signal Process. J. 89(7), 1334–1346 (2009)
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)
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)
Li S., Yang B.: Multifocus image fusion by combining curvelet and wavelet transform. Pattern Recognit. Lett. 29, 1295–1301 (2008)
Choi M., Kim R.Y., Kim M.G.: The curvelet transform for image fusion. Int. Soc. Photogrammetry Remote Sens. 35(B8), 59–64 (2004)
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)
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)
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
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)
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)
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)
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
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
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)
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)
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)
Burt, P.J.: A gradient pyramid basis for pattern selective image fusion, Society for Information Display. Digest of Technical Papers, 467–470 (1992)
Li H., Manjunath B.S., Mitra S.K.: Multisensor image fusion using the wavelet transform. Graph. Models Image Process. 57(3), 235–245 (1995)
Pajares G., Cruz J.: A wavelet-based image fusion tutorial. Pattern Recognit. 37(9), 1855–1872 (2004)
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)
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
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)
Do M.N., Vetterli M.: Contourlets in beyond wavelets. In: Welland, G.V. (ed.) Contourlets, Beyond Wavelets, Academic Press, New York (2003)
Petrovic V., Xydeas C.: Objective image fusion performance characterisation. Proc. ICCV’05 2, 1866–1871 (2005)
Qu G., Zhang D., Yan P.: Information measure for performance of image fusion. Electron. Lett. 38(7), 313–315 (2002)
Toet A., Ijspeert J.K., Waxman A.M., Aguilar M.: Perceptual evaluation of different image fusion schemes. Displays 24, 25–37 (2003)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-011-0219-7