Wavelets for Image Fusion

  • Stavri Nikolov
  • Paul Hill
  • David Bull
  • Nishan Canagarajah
Part of the Computational Imaging and Vision book series (CIVI, volume 19)


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.


Input Image Discrete Wavelet Transform Image Fusion Wavelet Coefficient Edge Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Stavri Nikolov
    • 1
  • Paul Hill
    • 1
  • David Bull
    • 1
  • Nishan Canagarajah
    • 1
  1. 1.Centre for Communications ResearchUniversity of BristolBristolUK

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