Transforming a thermal infrared image into a realistic RGB image is a challenging task. In this paper we propose a deep learning method to bridge this gap. We propose learning the transformation mapping using a coarse-to-fine generator that preserves the details. Since the standard mean squared loss cannot penalize the distance between colorized and ground truth images well, we propose a composite loss function that combines content, adversarial, perceptual and total variation losses. The content loss is used to recover global image information while the latter three losses are used to synthesize local realistic textures. Quantitative and qualitative experiments demonstrate that our approach significantly outperforms existing approaches.

中文翻译：

---

通过条件生成对抗网络的热红外着色

将热红外图像转换为逼真的 RGB 图像是一项具有挑战性的任务。在本文中，我们提出了一种深度学习方法来弥合这一差距。我们建议使用保留细节的粗到细生成器来学习转换映射。由于标准均方损失不能很好地惩罚彩色图像和真实图像之间的距离，我们提出了一种复合损失函数，它结合了内容、对抗性、感知和总变化损失。内容损失用于恢复全局图像信息，而后三个损失用于合成局部逼真纹理。定量和定性实验表明，我们的方法明显优于现有方法。