# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 1 Introduction  
The traditional source of infrared image data is through an infrared camera device that uses the temperature difference between the target and the background to capture infrared light and generate infrared images. The advantage of infrared camera image acquisition is that it can be adopted to locate and track targets at night or in harsh weather. However, infrared images acquired in such harsh environments tend to be of poor quality, with severe loss of textured detail, and expensive infrared equipment, and are thus less used in civilian applications. With the rapid development of deep learning, infrared image generation using neural networks has received increasing attention from researchers. Infrared images obtained through using deep learning methods cannot only compensate for defects in infrared cameras employed in harsh environments but also save costs. Therefore, infraredimages generated by neural networks are extensively employed in military research and civilian applications. In recent years, deep neural networks have been widely used in various fields, such as target detection, computer vision, and image migration [1; 2]. Based on the idea of gaming, in 2014, [PERSON] et al. proposed generative adversarial networks [3; 4], representing one of the widely applied methods in deep learning at present. Generative adversarial networks include generator networks and discriminator networks. The basic idea is that the task generator generates fake data and the discriminator's task is to discriminate fake data generated by the generator from the real data; in essence, to distinguish real data from fake data. The generative adversarial network achieves the conversion of source-domain images to target-domain images based on this game idea. Nevertheless, researchers have found that the network suffers from easy collapse and limited image generation capability in model training. In the field of image generation research, in order to achieve better image generation techniques and to obtain more realistic images in the target domain, researchers have proposed many new ideas and methods for generative adversarial networks to overcome problems and difficulties in image generation techniques. In the area of supervised image generation, [PERSON] and [PERSON] et al. proposed the deep convolutional generative adversarial network [5; 6; 7] (DCGAN) in 2015. Compared to traditional generative adversarial networks, deep convolutional generative adversarial networks use leaky ReLU activation function and provide better enhancement of the generated image features. However, the problem with DCGAN networks in image generation is that training requires paired datasets that are small and difficult to collect under normal circumstances. In 2017, [PERSON] et al. proposed a supervised-learning-based pix2 pix network based on pix2 pix image generation algorithm [8]. The pix2 pix algorithm was based on CGAN, using UNet as the generator of the network and PatchGAN as the discriminator to reconstruct accuracy through reconstruction loss and adversarial joint optimization of low and high frequencies. The method achieves generation from semantically segmented images to visible images and can achieve more desirable results in image generation tasks, yet the method is similar to the DCGAN method in that it requires the use of paired datasets to generate images from the source domain to the target domain, making the preliminary data collection workload particularly high. Meanwhile, the pix2 pix algorithm produces unsatisfactory images when the input dataset deviates significantly from the training dataset. To resolve the problems of the pix2 pix algorithm, [PERSON] et al. proposed the pix2 pixHD algorithm [9; 10; 11] in 2018 to generate high-definition images. The pix2 pixHD algorithm was optimized based on the pix2 pix algorithm. The authors adopted a global generative network and a local boosting network in the generator and by optimizing the generator network, 2048*1024 HD images could be generated.