Infrared and visible image fusion (IVIF) is a pivotal technology in low-altitude Unmanned Aerial Vehicle (UAV) reconnaissance missions, enabling robust target detection and tracking by integrating thermal saliency with environmental textures. However, traditional no-reference metrics (Statistics-based metrics and Gradient-based metrics) fail in complex low-light environments, termed the ``Noise Trap''. This paper mathematically prove that these metrics are positively correlated with high-frequency sensor noise, paradoxically assigning higher scores to degraded images and misguiding algorithm optimization. To address this, we propose the Target-Background Contrast (TBC) metric. Inspired by Weber's Law, TBC focuses on the relative contrast of salient targets rather than global statistics. Unlike traditional metrics, TBC penalizes background noise and rewards target visibility. Extensive experiments on the DroneVehicle dataset demonstrate the superiority of TBC. Results show that TBC exhibits high ``Semantic Discriminability'' in distinguishing thermal targets from background clutter. Furthermore, TBC achieves remarkable computational efficiency, making it a reliable and real-time standard for intelligent UAV systems.

翻译：红外与可见光图像融合（IVIF）是低空无人机侦察任务中的关键技术，通过整合热辐射显著性与环境纹理，实现鲁棒的目标检测与跟踪。然而，传统无参考度量（基于统计的度量和基于梯度的度量）在复杂低光环境下失效，此现象被称为“噪声陷阱”。本文从数学上证明这些度量与高频传感器噪声呈正相关，矛盾地为退化图像赋予更高评分，从而误导算法优化。为解决此问题，我们提出目标-背景对比度（TBC）度量。受韦伯定律启发，TBC聚焦于显著目标的相对对比度而非全局统计量。与传统度量不同，TBC惩罚背景噪声并奖励目标可见性。在DroneVehicle数据集上的大量实验证明了TBC的优越性。结果表明，TBC在区分热目标与背景杂波方面表现出高度的“语义可区分性”。此外，TBC实现了显著的计算效率，使其成为智能无人机系统可靠且实时的评估标准。