Skip to main content

Part of the book series: Computational Imaging and Vision ((CIVI,volume 19))

Abstract

In this chapter we present some recent results on the use of wavelet algorithms for image fusion. The chapter starts with a brief introduction of image fusion. The following sections describe three different wavelet transforms and the way they can be employed to fuse 2-D images. These include: the discrete wavelet transform (DWT); the dual-tree complex wavelet transform (DT-CWT); and Mallat’s discrete dyadic wavelet transform (DDWT), which can also be used to compute a multiscale edge representation of an image. The three wavelet fusion schemes are compared both qualitatively and quantitatively and are applied to fuse multifocus, remote sensing and medical (CT and MR) images. The experimental comparison clearly shows that DT-CWT fusion techniques provide better results than their DWT counterparts. In addition, the use of DT-CWT gives control over directional information in the images, while the use of multiscale edge fusion methods provides control over the edge information to be retained in the fused output. The chapter concludes with a discussion about the strong points and difficulties associated with each of the proposed wavelet fusion schemes and with some ideas for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Abidi, M. A. and Gonzalez, R. C., editors (1992). Data Fusion in Robotics and Machine Intelligence. Academic Press.

    MATH  Google Scholar 

  • Burt, P. J. and Kolczynski, R. J. (1993). Enhanced image capture through fusion. Proceedings of the 4th International Conference on Computer Vision, pages 173–182.

    Google Scholar 

  • Chipman, L. J., Orr, T. M., and Lewis, L. N. (1995). Wavelets and image fusion. In Proceedings IEEE International Conference on Image Processing, Washington D. C., volume 3, pages 248–251. IEEE.

    Chapter  Google Scholar 

  • Daubechies, I. (1992). Ten Lectures on Wavelets. SIAM, Philadelphia, PA. Notes from the 1990 CBMS-NSF Conference on Wavelets and Applications at Lowell, MA.

    Book  MATH  Google Scholar 

  • Kingsbury, N. G. (1998). The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters. IEEE Digital Signal Processing Workshop, (paper 86).

    Google Scholar 

  • Kingsbury, N. G. (2000). A dual-tree complex wavelet transform with improved orthogonality and symmetry properties. Proc. IEEE Conf. on Image Processing, Vancouver, September 11–13, 2000, (paper 1429).

    Google Scholar 

  • Koren, I. and Laine, A. (1998). A discrete dyadic wavelet transform for multidimensional feature analysis. In Akay, M., editor, Time Frequency and Wavelets in Biomedical Signal Processing, pages 425–449. IEEE Press.

    Google Scholar 

  • Koren, I., Laine, A., and Taylor, F. (1995). Image fusion using steerable dyadic wavelet transforms. In Proceedings IEEE International Conference on Image Processing, Washington D.C., pages 232–235. IEEE.

    Chapter  Google Scholar 

  • Koren, I., Laine, A., and Taylor, F. (1998). Enhancement via fusion of mammographic features. In Proceedings IEEE International Conference on Image Processing, Chicago, Illinois, volume 1, pages 722–726. IEEE.

    Google Scholar 

  • Li, H., Manjunath, B. S., and Mitra, S. K. (1995). Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57(3):235–245.

    Article  Google Scholar 

  • Lu, J. (1993). Signal recovery and noise reduction with wavelets. PhD thesis, Dartmouth College, Hanover, New Hampshire.

    Google Scholar 

  • Mallat, S. and Hwang, W. L. (1992). Singularity detection and processing with wavelets. IEEE Trans. Inform. Theory, 38:617–643.

    Article  MathSciNet  MATH  Google Scholar 

  • Mallat, S. and Zhong, S. (1990). Wavelet Transform Maxima and Multiscale Edges. Bartlett and Jones. eds. Coifman et al.

    Google Scholar 

  • Mallat, S. and Zhong, S. (1992). Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(7):710–732.

    Article  Google Scholar 

  • Moigne, J. L. and Cromp, R. F. (1996). The use of wavelets for remote sensing image registration and fusion. Technical Report TR-96–171, NASA Goddard Space Flight Center.

    Google Scholar 

  • Nikolov, S. G., Bull, D. R., Canagarajah, C. N., Halliwell, M., and Wells, P. N. T. (2000a). Fusion of 2-D images using their multiscale edges. In 15th International Conference on Pattern Recognition, Barcelona, Catalonia, Spain, 3–8 September, volume 3, pages 45–48. IEEE Computer Science Press.

    Google Scholar 

  • Nikolov, S. G., Bull, D. R., Canagarajah, C. N., Halliwell, M., and Wells, P. N. T. (2000b). 2-D image fusion by multiscale edge graph combination. In 3rd International Conference on Information Fusion (Fusion 2000), Paris, France, 10–13 July, volume I, pages MoD3–16-22. International Society of Information Fusion (ISIF).

    Google Scholar 

  • Petrovic, V. and Xydeas, C. (1999). Cross band pixel selection in multiresolution image fusion. In Proceedings of SPIE, volume 3719, pages 319–326. SPIE.

    Google Scholar 

  • Rockinger, O. (1996). Pixel-level fusion of image sequences using wavelet frames. In Mardia, K. V., Gill, C. A., and Dryden, I. L., editors, Proceedings in Image Fusion and Shape Variability Techniques, Leeds, UK, pages 149–154. Leeds University Press.

    Google Scholar 

  • Rockinger, O. (1997). Image sequence fusion using a shift invariant wavelet transform. In Proceedings of the IEEE International Conference on Image Processing, volume III, pages 288–291. IEEE.

    Chapter  Google Scholar 

  • Wilson, T. A., Rogers, S. K., and Myers, L. R. (1995). Perceptual based hyperspectral image fusion using multiresolution analysis. Optical Engineering, 34 (11) :3154–3164.

    Article  Google Scholar 

  • Zhang, Z. and Blum, R. (1999). A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proceedings of the IEEE, pages 1315–1328.

    Google Scholar 

  • Zhou, J., Civco, D. L., and Silander, J. A. (1998). Wavelet transform method to merge Landsat TM and SPOT panchromatic data. International Journal of Remote Sensing, 19 (4) :743–757.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Nikolov, S., Hill, P., Bull, D., Canagarajah, N. (2001). Wavelets for Image Fusion. In: Petrosian, A.A., Meyer, F.G. (eds) Wavelets in Signal and Image Analysis. Computational Imaging and Vision, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9715-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-94-015-9715-9_8

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5838-6

  • Online ISBN: 978-94-015-9715-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics