基于对抗博弈策略的无人机红外图像自适应融合算法优化设计OA

To address the challenges of insufficient real-time performance,loss of target features,and difficulty in suppressing background artifacts during unmanned aerial vehicle(UAV)infrared-visible image fusion in complex environments,an optimized adaptive fusion algorithm based on adversarial game strategies was proposed in this study.Built on the lightweight RFN-Nest backbone,the method established a generator-discriminator adversarial framework.The generator employed channel-spatial dual attention to dynamically weight infrared thermal targets and visible textures in real time,whereas the discriminator supervised global realism with real fused images.Furthermore,an infrared-statistics-based mask weighted loss was introduced to enhance weak thermal targets,achieving high-precision real-time fusion within 20 M parameters and enabling deployment on edge devices.On the public TNO dataset,compared with the second-best method,ROIMaskCNN,the proposed approach increased entropy(EN)by 1.8%,standard deviation(SD)by 5.1%,and mutual information(MI)by 5.0%;additionally,it reduced the non-artifact blur factor(Nabf)by 9.4%and improved multi-scale structural similarity(MS-SSIM)by 0.7%,demonstrating clear superiority.