# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 4 Analysis of Experimental Results
### Experimental Results
As seen from the visualization results in the first data set (first row) of Figure 4, the image generated by original method suffers from blurred white shadows and a severe lack of detail in the region marked in red, with a poor degree of infrared image effect. In the ResNet experiment, there is a small amount of white shading in the edge information at the image details, and the ability to generate more texture detail information is significantly improved compared to the original method, with a small amount of detail information loss. From our proposed method, the generated infrared image is rich in texture detail, with more apparent contour information, and is close to the real picture. In the second set of experiments, the original method has more white shadows and severe feature loss in the region marked in red. In the ResNet experiments, there are only a few white shadows, which are more blurred in the image, and there is a large loss of detailed information. In the cut method, there is no good generation for all three sample datasets; the generated images' quality is more blurred, and the infrared effect is not apparent. In comparison with the two previous methods, the method in this paper produces images with no white shadows, significant feature information, and no loss of detail information. In the third data group, the red-marked area is the road line. Compared with the real infrared image, the original method and the method based on ResNet network cannot generate the features in the red area well. While the method in this paper can show better area feature information, correctly-represented texture details, and a better image-generation effect. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **OSU** & \multicolumn{2}{c}{**Flir**} \\ \hline Evaluation indicators & PSNR/dB & SSIM & PSNR/dB & SSIM \\ CycleGAN+unet\_128 & 13.6867 & 0.2479 & 13.2131 & 0.4167 \\ CycleGAN+unet\_256 & 13.7892 & 0.2686 & 12.7318 & 0.4444 \\ CycleGAN+resnet\_6 blocks & 17.3956 & 0.7071 & 13.3745 & 0.4700 \\ CycleGAN+resnet\_9 blocks & 17.0300 & 0.6900 & 13.3275 & 0.4368 \\ CUT [42] & 13.3502 & 0.3168 & 13.1813 & 0.4214 \\ Ours(GN\_CycleGAN) & 18.0699 & 0.7491 & 13.5195 & 0.4572 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Objective evaluation results of the comparative experiment. As seen from the visualization results in the first data set (first row) of Figure 5, in the CycleGAN+Unet_128 (original) experiment, there is a severe lack of detailed information within the lower left red annotated region compared to the real IR image, and the features in the image are not recognizable. In the ResNet generator network experiments, the image is able to exhibit good discrimination, but there are more heavily shaded overlapping feature sections within the upper right red-labeled area. In the cut method, the resulting image is blurred, and the texture detail is severely missing. In our proposed method, the shortcomings of the two experiments mentioned above are overcome within the red-labelled regions, generating highly discriminative images, rich in detailed information generation, and closest to the real IR image. In the red region of the second data set, the other methods generate images with severe information loss in the red region, while our proposed method is able to avoid the loss of detailed information. The red area of the third data set shows the ground texture detail information. Through observation, it suggests that the other methods that generate the ground image texture clarity are not as good as the proposed method. To address the problem prone to collapse of the current model training process, a gradient normalization method is used to verify the stability of the model training in this paper by testing the overall direction of the cyclic loss function values of generator A and generator B during the training process. In this paper, the original CycleGAN method and the CBAM_CycleGAN method, which introduces an attention mechanism, are used as control groups.