# Welding Image Data Augmentation Method Based on LRGAN Model
## 4 Experimental Results and Discussion  
### Ablation Experiment  
This paper uses ablation experiments to validate the performance improvement of different modifications in the LRGAN model. Here, GAN refers to the original generative adversarial network; LS-GAN represents the model where the least squares loss function is introduced on top of GAN; LN-GAN refers to the model where spectral normalization is introduced into LS-GAN; RGAN refers to the model where residual networks are incorporated into GAN; and LRGAN is the model proposed in this paper. The experimental results of these five methods on the training set are shown in Table 3 and Figure 12.  
As shown in Figure 12, compared to GAN, LS-GAN improves PSNR by 5.9%, reduces FID by 4.9%, and the SSIM value is 0.47. Furthermore, after adding spectral normalization, the PSNR of the LN-GAN model increases by 59.7%, FID decreases by 10.6%, and the SSIM value is 0.76. Therefore, it can be seen that spectral normalization has a more significant effect on improving PSNR. Compared to LN-GAN, RGAN improves PSNR by 10.6%, reduces FID by 17.8%, and the SSIM value is 0.66. The FID value is smaller and decreases more quickly, indicating that the residual structure is more helpful in reducing the FID value. In comparison to RGAN, LRGAN improves PSNR by 37.1%, and reduces FID by 32.5%, with the smallest error bars among all groups. Additionally, in the five comparison experiments, LRGAN achieves the highest PSNR, the lowest FID, and an SSIM value of 0.82, which is closest to 1, indicating that the generated images in this group are most similar to the original welding images. To further validate the effectiveness of the improvement methods, experiments were also conducted on the test set, and the results are shown in Figure 13. LRGAN has the smallest FID value, and the highest PSNR and SSIM, with the smallest error in this group. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Metrics** & **Evaluation Method** & **Numerical Representation** \\ \hline PSNR & Reflecting the error between corresponding pixel points in the image & A larger value indicates better quality of the image being evaluated \\ \hline SSIM & Measuring the generated image from three aspects: luminance, contrast, and structure & The range of structural similarity (SSIM) is from 0 to 1; the closer the value is to 1, the more similar the image is \\ \hline FID & Measuring the feature distance between real and generated images, by calculating the distribution distance between the generated image and the real image to evaluate the quality of the image \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Methods** & **FID** & **PSNR** & **SSIM (\%)** \\ \hline
1 & GAN & 60.01 & 32.07 & 32.00 \\
2 & LS-GAN & 57.07 & 33.98 & 47.00 \\
3 & LN-GAN & 51.01 & 54.26 & 76.00 \\
4 & RGAN & 41.95 & 60.03 & 66.00 \\
5 & LRGAN & \multirow{2}{*}{(Our)} & \multirow{2}{*}{28.31} & \multirow{2}{*}{82.33} & \multirow{2}{*}{82.00} \\  & & & & \\ \end{tabular}
\end{table} 
Table 3: Ablation experiment. A comparative analysis shows that LRGAN performs the best. From the error bars in the figure above, it can be seen that the overall error from GAN to LRGAN generally shows a gradual downward trend, with the three evaluation metrics in LRGAN having the smallest errors. The experimental results on the test set further confirm that using the least squares loss function as the adversarial network loss function, as well as adopting spectral normalization and residual structures, all contribute to improving the quality of images generated by LRGAN. Spectral normalization has a more significant effect on improving PSNR, while the residual structure is more helpful in reducing the FID value. The least squares loss function plays the largest role in controlling SSIM, making its value closest to 1 (as shown in the figure above, which represents the value closest to 100%).