# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 4 Analysis of Experimental Results
### Ablation Experiments  
This paper conducts ablation experiments using the Flir dataset to analyze the role of the proposed method in the network structure. The experiments consist of 4 parts.  
(1) CycleGAN (baseline): the original method network.  
(2) CycleGAN+ResNet: a residual network used in the generator apparatus.  
(3) CycleGAN+ResNet+CBAM: a residual network incorporating an attention mechanism is used in the generator.  
(4) CycleGAN+ResNet+CBAM+Gradients Normalization(Ours): the gradient normalized cycle generative adversarial network method proposed in this paper.  
The statistical results are shown in Table 3. The table shows that, compared to the original method, the PSNR and SSIM metrics, where the residual network was introduced, improved slightly in the experiments. In the experiments with the residual network incorporating the attention mechanism, the PSNR values improved slightly, and the SSIM values improved more than the original method. In the experimental method proposed in this paper, there is an improvement of 2.32% and 9.72% in the PSNR and SSIM metrics, respectively, compared to other methods.  
The results of the ablation experiments are shown in Figure 8. As seen from the figure, the first group data (first row) in the CycleGAN experiment, compared with the original real IR image, differs significantly from the real data in the red annotated region, and the CycleGAN method has a significant feature loss to generate images with a large degree of blurring. Compared with the original method, the red annotated regions of the first  
Figure 7: Visualisation of the cycle loss gradient results for generator_B.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PSNR/dB** & **SSIM** \\ \hline Baseline(CycleGAN) & 13.2131 & 0.4167 \\ CycleGAN+ResNet & 13.3275 & 0.4368 \\ CycleGAN+ResNet+CBAM & 13.2403 & 0.4530 \\ Ours (GN\_CycleGAN) & 13.5195 & 0.4572 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of objective evaluation indicators for ablation experiments.  
group data in the experiment with the introduction of ResNet generator show less feature loss, and the image details are not sufficiently detailed. By introducing our proposed gradient normalization network method, the loss in the red region of the first dataset is minimal, and the generated image features are more adequate and closest to the original real image. In the second (second row) and third (third row) datasets, compared with the real data, the images generated by the Cyclegan method have insufficient texture detail information in the red annotated area as well as a large loss, and the generated images are blurred with some white shadows resulting in poor image visibility. In the experiments with the introduction of ResNet generator, the quality of generated images is better, with no white shadows and good image visibility. In the experiments after introducing the ResNet generator, the quality of generated images is better. There is no white shadow, and the image visibility is good, but in terms of detail generation, the images from the ResNet generator experiments differ significantly from the real images. In our proposed gradient normalization method, the loss of texture detail information in the generated image quality is minimal, the best image quality is obtained, and the detail information generation is more prosperous.  
The visualization results of ablation tests and the evaluation index data show that our proposed method has a more remarkable improvement in the generation of image texture details, and that the generated infrared images are closest to the real infrared images, thus better validating the effectiveness of our method.