Mimic Fusion Method for Differences in Dual-Mode Infrared Images

Traditional fusion methods cannot select an effective fusion strategy based on the different characteristics of dual-mode infrared images. A mimic fusion method for the difference between the infrared intensity and polarization images was developed in this study. First, the degree of difference between image features was calculated to roughly screen the difference features, and the selection rules of the main difference feature types were formulated to determine the main difference features of the image groups. Next, the degree of feature fusion was constructed to establish the mapping between the difference features and variables in each layer of the mimic variable set and to determine the hierarchical structure of the variables. Finally, in the hierarchical structure of the variables, the variables of each layer of the main difference feature type were selected. The degrees of feature fusion of the difference features between combined variables of different mimic structures were compared to determine the mimic structure with the highest proportion of its maximum value and form a variant. The experimental results show that the visual effect of the proposed method was better than that of the comparison method after a subjective analysis. After objective evaluation, the results obtained using the proposed method indicate effective fusion. Therefore, this method realizes adaptive selection of the fusion strategy and improves image fusion quality.