MAF: An algorithm based on multi-agent characteristics for infrared and visible video fusion

Area of Science:

• Computer Vision
• Artificial Intelligence
• Image Processing

Background:

• Existing infrared and visible video fusion models lack dynamic adaptation, leading to suboptimal performance.
• Traditional methods struggle with varying video content and fail to adjust fusion strategies effectively.

Purpose of the Study:

• To develop a dynamic infrared and visible video fusion algorithm using multi-agent systems.
• To overcome the limitations of static fusion strategies in existing models.

Main Methods:

• Constructed a multi-agent fusion model with specialized fusion agents.
• Identified regions of interest and key distinguishing features in video frames.
• Implemented a collaborative agent-based approach for real-time fusion strategy selection and execution.

Main Results:

• The proposed method outperforms existing approaches in preserving infrared targets and visible structural details.
• Achieved optimal fusion outcomes in 66.7% of cases, significantly surpassing traditional single fusion methods.
• Demonstrated superior performance with 80.9% of sub-optimal and higher values.

Conclusions:

• The multi-agent system provides a dynamically adaptable framework for infrared and visible video fusion.
• This approach enables real-time adjustments, leading to superior fusion quality and performance.
• The method offers a significant advancement over conventional static fusion techniques.