TBC: A Target-Background Contrast Metric for Low-Altitude Infrared and Visible Image Fusion

TL;DR

This paper introduces the Target-Background Contrast (TBC) metric for infrared and visible image fusion in UAV reconnaissance, effectively addressing noise issues and improving target detection accuracy in low-light environments.

Contribution

The paper proposes a novel TBC metric inspired by Weber's Law that outperforms traditional no-reference metrics in complex low-light conditions for UAV image fusion.

Findings

TBC effectively penalizes background noise and highlights targets.

TBC demonstrates high semantic discriminability between targets and background.

TBC is computationally efficient for real-time UAV applications.

Abstract

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.