提出可任意缩放的热成像超分辨率方法，提升无人机在复杂环境下的图像质量。
AnyTSR: Any-Scale Thermal Super-Resolution for UAV
- 设计新编码器和可变尺度上采样器，实现单模型多尺度热成像增强
- 在多个缩放因子下均超越现有方法，细节更清晰、边界更锐利
- 构建包含陆地与水域场景的UAV-TSR数据集，适配无人机热成像应用
热成像能显著提升智能无人机在复杂环境中的应用能力。然而，热传感器固有的低分辨率导致细节不足、边界模糊。超分辨率（SR）为此提供有效解决方案，但现有方法多针对固定尺度，计算成本高且实用性差。本文提出一种新型任意尺度热成像超分辨率方法（AnyTSR），适用于无人机场景。创新性地设计图像编码器，通过显式特征编码实现更精准灵活的表征；通过将坐标偏移信息嵌入局部特征集成，提出新型任意尺度上采样器，更好理解空间关系并减少伪影。同时构建了涵盖陆地与水域场景的新数据集UAV-TSR，用于热成像超分辨率任务。实验表明，该方法在所有缩放因子下均持续优于现有先进方法，生成的高分辨率图像更准确、细节更丰富。代码已开源：https://github.com/vision4robotics/AnyTSR。
原文摘要 · Abstract (English)
Thermal imaging can greatly enhance the application of intelligent unmanned aerial vehicles (UAV) in challenging environments. However, the inherent low resolution of thermal sensors leads to insufficient details and blurred boundaries. Super-resolution (SR) offers a promising solution to address this issue, while most existing SR methods are designed for fixed-scale SR. They are computationally expensive and inflexible in practical applications. To address above issues, this work proposes a novel any-scale thermal SR method (AnyTSR) for UAV within a single model. Specifically, a new image encoder is proposed to explicitly assign specific feature code to enable more accurate and flexible representation. Additionally, by effectively embedding coordinate offset information into the local feature ensemble, an innovative any-scale upsampler is proposed to better understand spatial relationships and reduce artifacts. Moreover, a novel dataset (UAV-TSR), covering both land and water scenes, is constructed for thermal SR tasks. Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art methods across all scaling factors as well as generates more accurate and detailed high-resolution images. The code is located at https://github.com/vision4robotics/AnyTSR.
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