# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 5 Discussion  
In the current mainstream algorithms for generating infrared images from visible images, we have found that the current methods suffer from poor image quality, loss of texture detail and lack of model stability during the training process. Researchers have made outstanding contributions to address some of these problems. Our work focuses on the inability of infrared cameras to acquire high-quality infrared images with rich texture detail in harsh environments, and uses deep learning methods to compensate for the shortcomings. We propose a gradient-normalized recurrent generative adversarial network  
Figure 8: Results of ablation experiments for the Flir dataset.  
approach for IR images by a ResNet residual network with better image feature extraction capabilities. To reduce the loss of texture details, we add a spatial attention and channel attention mechanism to the generator network, highlighting the ability to perceive image detail regions to improve image generation quality, and to prevent the training model from crashing. In this paper, we introduce a gradient normalization module in the discriminator to speed up the model convergence and maintain the model training stability. The method achieves excellent performance in terms of image quality, texture detail and model stability. It outperforms the current mainstream CycleGAN method in the structural similarity metrics and peak signal-to-noise ratio metrics. In particular, it yields more satisfactory data results on the OSU colour-heat dataset with a 32.2% improvement in peak signal-to-noise ratio metric and an improvement from 0.2479 to 0.7491 in structural similarity. Meanwhile, on the FLIR dataset, our proposed method achieves a 2.3% improvement in peak signal-to-noise ratio, and improves the peak S/N ratio by 2.3% and the structural similarity by 9.7%.  
In order to address the problems of low image quality, lack of detail in texture information and unstable model training in the field of infrared image generation, the solution proposed in this paper is compared to the current mainstream generative networks. For example, the method has an advantage in data results compared to the Sparse GANS IR image generation method [43], which mainly focuses on the lack of prominent texture details resulting in poor image quality. The method in this paper is more stable than the conditional generative adversarial networks [44], which have better image generation capabilities but prone to collapse during model training. In contrast to previous work on image generation, we introduce for the first time a gradient normalization module into the field of image generation methods for recurrent generative adversarial networks to stabilise the model training process. The approach in this paper, focusing on the quality of image generation and the model stability, can achieve a good image quality to a certain extent, but the proposed approach also has shortcomings, such as neglecting the cost of model training. The future work will focus on improving the model efficiency with a balance between image quality and model stability.