This paper addresses the problem of translating night-time thermal infrared images, which are the most adopted image modalities to analyze night-time scenes, to daytime color images (NTIT2DC), which provide better perceptions of objects. We introduce a novel model that focuses on enhancing the quality of the target generation without merely colorizing it. The proposed structural aware (StawGAN) enables the translation of better-shaped and high-definition objects in the target domain. We test our model on aerial images of the DroneVeichle dataset containing RGB-IR paired images. The proposed approach produces a more accurate translation with respect to other state-of-the-art image translation models. The source code is available at https://github.com/LuigiSigillo/StawGAN

翻译：本文针对夜间热红外图像（分析夜间场景最常用的图像模态）到日间彩色图像（NTIT2DC）的翻译问题展开研究，后者能为物体提供更优的感知效果。我们提出了一种新型模型，其核心在于提升目标生成质量，而非仅进行简单的着色处理。提出的结构感知生成对抗网络（StawGAN）能够在目标域中翻译出形状更优、清晰度更高的物体。我们在包含RGB-IR配对图像的DroneVeichle数据集上对模型进行了航拍图像测试。实验结果表明，相较于其他前沿图像翻译模型，本方法能实现更精准的翻译效果。源代码已开源至https://github.com/LuigiSigillo/StawGAN