Joint Possibility Drop Shadow Construction for Selection of Bimodal Infrared Image Fusion Algorithm

A joint likelihood drop shadow construction method for the selection of a bimodal infrared image fusion algorithm is proposed. It aims at the demand for the cooperative and optimal fusion of dissimilar disparity features in real scenes of bimodal infrared image fusion and the limitation that the existing disparity feature attributes cannot be effectively driven by the targeted adjustment of the fusion algorithm according to the changes in multiple attributes of the disparity features, resulting in a poor fusion effect. First, we calculate the fusion effectiveness of different disparity features under the multimodal infrared image fusion algorithm and statistical disparity feature distribution characteristics. We then construct the likelihood distribution of the disparity feature fusion effectiveness and fit the likelihood distribution function by the least squares estimation method. Subsequently, we compare and analyze the likelihood distribution of different disparity feature fusion effectiveness by the merit comparison method and determine the projection weights of the disparity feature likelihood distribution function. Finally, we analyze the intercept level of the joint possibility drop shadow function and construct the optimal fusion algorithm by combining the characteristics of the distribution of different features to dynamically select the fusion performance index. The experimental results show that the optimal fusion algorithm selected in this study outperforms other algorithms in terms of subjective and objective analyses, which verifies the effectiveness and rationality of applying the joint likelihood drop shadow to the selection of an optimal fusion algorithm for bimodal infrared images.