A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory

Abstract

In response to the current practical fusion requirements for infrared and visible videos, which often involve collaborative fusion of difference feature information, and model cannot dynamically adjust the fusion strategy according to the difference between videos, resulting in poor fusion performance, a mimic fusion algorithm for infrared and visible videos based on the possibility distribution synthesis theory is proposed. Firstly, quantitatively describe the various difference features and their attributes of the region of interest in each frame of the dual channel video sequence, and select the main difference features corresponding to each frame. Secondly, the pearson correlation coefficient is used to measure the correlation between any two features and obtain the feature correlation matrix. Then, based on the similarity measure, the fusion effective degree distribution of each layer variables for different difference features is constructed, and the difference feature distribution is correlated and synthesized based on the possibility distribution synthesis theory. Finally, optimize the select of mimic variables to achieve mimic fusion of infrared and visible videos. The experimental results show that the proposed method achieve significant fusion results in preserving targets and details, and was significantly superior to other single fusion methods in subjective evaluation and objective analysis.