Vector Fusion of Multispectral Images Based on Multiwavelet Decomposition

Wu Xiao-rong; He Ming-yi; Zhang Yi-fan

doi:10.3724/SP.J.1146.2005.01127

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Wu Xiao-rong, He Ming-yi, Zhang Yi-fan. Vector Fusion of Multispectral Images Based on Multiwavelet Decomposition[J]. Journal of Electronics & Information Technology, 2007, 29(4): 789-794. doi: 10.3724/SP.J.1146.2005.01127

Citation:

Wu Xiao-rong, He Ming-yi, Zhang Yi-fan. Vector Fusion of Multispectral Images Based on Multiwavelet Decomposition[J]. Journal of Electronics & Information Technology, 2007, 29(4): 789-794. doi: 10.3724/SP.J.1146.2005.01127

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Vector Fusion of Multispectral Images Based on Multiwavelet Decomposition

doi: 10.3724/SP.J.1146.2005.01127

Received Date: 2005-09-06
Rev Recd Date: 2006-03-13
Publish Date: 2007-04-19

Abstract

Abstract

In the real domain, the finitely supported, orthogonal, symmetric nontrivial scalar wavelet bases do not exist, while the multiwavelet offers the finite support, symmetry, orthogonality simultaneously. As a result, the wavelet theory is extended to vector field. Considering vector characteristics provided by the coefficients of the multiwavelet transformed image, pixel-based and region-based scalar fusion schemes are extended to vector case and a novel fusion algorithm is also proposed in this paper. The new algorithm is based on vector fusion scheme in multiwavelet domain, which makes sufficient use of the correlation among the components of multiwavelet transform coefficient vectors to improve fusion quality. The original algorithm is carried out with emphases on the novelty of the fusion algorithm and the demonstration by using real multispectral image compared with algorithms employing wavelet scalar fusion scheme. The experimental results demonstrate that the proposed multiwavelet vector fusion algorithm can obtain both better subjective vision characteristics and better objective evaluation indices and outperform the wavelet scalar fusion scheme. Accordingly it is testified that when applied in image fusion, multiwavelet is more suitable than wavelet to human vision system and it is of great potential to wide applications.

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References

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