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A Multiscale Image Fusion Algorithm Based on Joint Distribution of Source Coefficients

The essence of image fusion is to integrate information of source images based on the rule of complementarity and similarity. Conventional multiscale image fusion methods, which are based on a statistical model commonly focus on the statistics of single source, leading to a drawback of not displaying the joint statistical characteristics between the two sources. According to the multiscale decomposition, a new fusion algorithm was proposed for fully measuring the relationship between source images. The marginal statistics and the joint statistics of two fused wavelet coefficients are precisely simulated by the generalized Gaussian distribution (GGD) and the bivariate anisotropic Laplacian model, which will offer a more precise description of the independency and correlation between coefficients. Moreover, based on the built model a fusion rule was incorporated using the information theory. Experiments show that the proposed method performs very well with images from multimodal datasets and outperforms the conventional methods in terms of the fusion quality of outputs.