Thermal infrared texture generation is a promising infrared imaging framework for various applications. A novel thermal infrared texture generation algorithm, based on siamese semantic CycleGAN (SS-CycleGAN), is proposed for thermal infrared systems. Different from traditional infrared simulation frameworks, SS-CycleGAN depends on no extra environmental information, such as air temperature, humidity and radiation properties of objects. In other words, visible images could be directly transformed into thermal infrared images like using style transfer algorithms, after traffic scene has been fully understood through training CNN. In this paper, style transfer is firstly introduced for generating thermal textures from color visible images. Siamese semantic loss for visible-infrared transformation is designed and introduced to generate object-oriented thermal infrared textures, while maintaining high definition. Compared to other style transfer algorithms, SS-CycleGAN could generate reasonable thermal infrared textures with clear edge details, in traffic scenes.

热红外纹理生成是用于各种应用的有前途的红外成像框架。提出了一种基于暹罗语义CycleGAN（SS-CycleGAN）的新型热红外纹理生成算法。与传统的红外仿真框架不同，SS-CycleGAN不依赖额外的环境信息，例如物体的温度，湿度和辐射特性。换句话说，在通过训练CNN完全了解交通场景之后，可以像使用样式转换算法一样将可见图像直接转换为热红外图像。在本文中，首先引入样式转换以从彩色可见图像生成热纹理。设计并引入了用于可见-红外转换的暹罗语义损失，以在保持高清晰度的同时生成面向对象的热红外纹理。与其他样式转换算法相比，SS-CycleGAN可以在交通场景中生成具有清晰边缘细节的合理的红外纹理。