12 August 2013 Image fusion method based on bivariate alpha-stable model in multiwavelet domain
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J. of Electronic Imaging, 22(3), 033011 (2013). doi:10.1117/1.JEI.22.3.033011
Considering statistical characteristics of multiwavelet coefficient vectors, an image fusion method with a window-based salience measure for vector fusion is proposed in the multiwavelet domain. After analyzing joint probability distribution of multiwavelet coefficient vectors, we respect the joint probability distribution as a peaky and heavy tailed two-dimensional non-Gaussian distribution, and hence model it by using a bivariate symmetric alpha-stable (BiSαS) distribution. Model parameters of the BiSαS distribution can be estimated from samples of multiwavelet coefficient vectors. In a sliding window, the estimated parameters can reveal the impulsive shape of the histogram of the multiwavelet coefficient vectors within the window. So we employ these model parameters to form the new salience measure indicating the degree of prominent information. In our method, vector fusion rule is introduced to avoid inconsistency of decision maps between the component sub-bands and, consequently, to lessen ringing artifacts in final fusion results. Experimental results support the effectiveness of our proposed method in the area of visual quality and objective evaluations.
© 2013 SPIE and IS&T
Cai Xi, Zhao Wei, Jinkuan Wang, Han Guang, "Image fusion method based on bivariate alpha-stable model in multiwavelet domain," Journal of Electronic Imaging 22(3), 033011 (12 August 2013). https://doi.org/10.1117/1.JEI.22.3.033011

Image fusion



Infrared imaging

Statistical analysis

Continuous wavelet transforms

Discrete wavelet transforms

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