DFTP: A Mimic Fusion Method Driven by Difference Feature Time-Phase Perception for Infrared and Visible Video

Optimizing fusion strategy according to the difference information between videos is a necessary means to realize high-precision fusion of multisource videos. Addressing the limitations of existing video fusion models, which fail to intelligently adapt fusion strategies to dynamic changes in difference features over time, resulting in resource wastage, poor fusion quality, and even failure, we propose a mimic fusion method inspired by the versatile mimicry of the octopus. This method is driven by difference feature time-phase (TP) perception for infrared and visible video, namely DFTP. First, the comprehensive weight function of difference features is constructed by extracting the amplitude and frequency of difference features between bimodal videos, so as to determine the main difference feature types of video frames. Second, the corresponding possibility distribution vector subsets of each difference feature are obtained under the possibility framework, and the weighted synthetic of multiple subsets is realized based on the credibility (CR) and gap value (GA) of each subset. The comprehensive information function of the feature synthesis subset is constructed, and the TP value of the difference feature is calculated. Then, the mimic transformation mode of the current frame is determined by perceiving the TP changes of difference features between frames. Finally, the best combination of mimic variables is selected to realize the mimic fusion of infrared and visible video based on the mapping memory bank and fusion effectiveness function. The experimental results show that the proposed method not only retains the typical infrared targets and visible structural details of the video as a whole but also is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation.