AI 中文总结
该研究针对夜间航拍图像增强难题，构建AeroNight-1.5K基准，提出两阶段框架AeroLLE，结合约束伪监督实现更均衡的曝光与颜色校正，为无配准参考时的图像增强提供有效策略。
AI 中文摘要
夜间航拍图像增强面临空间非均匀曝光、混合光照及弱结构证据的挑战，而移动平台难以获取配准的正常光照目标。生成的正常光照图像可提供实用外观引导，但可能改变几何或纹理。我们推出AeroNight，包含1500张真实夜间航拍RGB图像：1300张输入关联人工筛选的伪参考，200张输入支持无配对评估。我们提出AeroLLE，这是一个两阶段框架，首先通过HVI Base Enhancer恢复可见性，然后执行空间自适应曝光-颜色校准（SAECC）。在选择并冻结Base Enhancer后，SAECC预测有界的低分辨率RGB增益和偏置场，限制第二阶段校正的幅度和空间变化。在互补的伪配对和无配对协议下的实验表明，与筛选的外观目标的一致性得到改善，同时在不同夜间航拍场景中实现更均衡的曝光和颜色校正。这些结果支持，在无法获取配准航拍参考时，采用约束的、阶段特定的校准作为从生成的外观引导中学习的实用策略。
英文摘要
Nighttime aerial image enhancement is challenged by spatially nonuniform exposure, mixed illumination, and weak structural evidence, while registered normal-light targets are difficult to capture from moving platforms. Generated normal-light images provide practical appearance guidance but may alter geometry or texture. We introduce \aeronight{}, comprising 1,500 real nighttime aerial RGB images: 1,300 inputs are associated with manually screened pseudo-references, and 200 inputs support unpaired evaluation. We propose AeroLLE, a two-stage framework that first recovers visibility with an HVI Base Enhancer and then performs Spatially Adaptive Exposure--Color Calibration (SAECC). After the Base Enhancer is selected and frozen, SAECC predicts bounded, low-resolution RGB gain and bias fields, restricting the magnitude and spatial variation of the second-stage correction. Experiments under complementary pseudo-paired and unpaired protocols demonstrate improved agreement with screened appearance targets, together with more balanced exposure and color correction across diverse nighttime aerial scenes. These results support constrained, stage-specific calibration as a practical strategy for learning from generated appearance guidance when registered aerial references are unavailable.
