提出新型图像融合评估指标，解决低空无人机成像中噪声干扰评分失真问题。
WLC: Weber-Inspired Local Contrast Metric for Low-Altitude Image Fusion
- 基于韦伯定律设计局部对比度指标，聚焦目标与背景的语义差异。
- 在DroneVehicle数据集上验证，能准确区分热源目标与背景杂波。
- 计算高效，适合实时智能无人机系统使用。
红外与可见光图像融合是低空无人机侦察任务的关键技术，可结合热辐射显著性与环境纹理实现稳定的目标检测与跟踪。然而，融合算法的发展受限于评价瓶颈。本文揭示传统无参考指标（统计类与梯度类）在复杂低光照环境下存在系统性失效，称为“噪声陷阱”——数学证明其与高频传感器噪声正相关，反而给劣化图像赋予更高评分，误导算法优化。为此，本文提出受韦伯定律启发的局部对比度（WLC）指标。该指标基于心理物理学原理，将评估范式从全局统计分布转为局部语义对比，利用红外先验有效分离目标显著性与全局背景噪声。在DroneVehicle数据集上的大量实验表明，WLC具备高“语义可区分性”，能有效识别热目标与背景杂波；同时具有显著计算效率，可作为智能无人机系统的可靠、实时评价标准。
原文摘要 · Abstract (English)
Infrared and visible image fusion 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, the advancement of fusion algorithms is hindered by a critical evaluation bottleneck. In this paper, we identify a systematic failure in traditional no-reference metrics (specifically Statistics-based and Gradient-based metrics) within complex low-light environments, termed as ``Noise Trap''. It is mathematically proven that these metrics are positively correlated with high-frequency sensor noise, paradoxically assigning higher scores to degraded images and misguiding algorithm optimization. To resolve this dilemma, this paper proposes the Weber-inspired Local Contrast (WLC) metric. Grounded in the psychophysical principle of Weber's Law, WLC shifts the evaluation paradigm from global statistical distribution to local semantic contrast. By leveraging infrared priors, it effectively decouples target saliency from global background noise. Extensive experiments on the DroneVehicle dataset demonstrate that WLC exhibits high ``Semantic Discriminability'' in distinguishing thermal targets from background clutter. Furthermore, it achieves remarkable computational efficiency, thus, establishing itself as a reliable and real-time standard for intelligent UAV systems
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