# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 4 Experiments and Results
### Results  
In the table, the best results of all metrics are marked in bold. When evaluating the quality of super-resolution images, a higher value of PSNR and SSIM represents better quality of the generated images, while a lower value of PI indicates better performance. In terms of evaluating the recognition effectiveness of iris images, a lower value of the EER denotes better effectiveness. We train SRCNN based on the CNN network model, ESRGAN based on the GAN network model, SwinIR and HAT based on the Transformer network model, as well as IrisDnet based on the GAN and the densely connected structure targeting the iris super-resolution model using the same training set while testing with the same testing set. Furthermore, a set of ablation experiments is conducted to examine the effect of GAN structure on the generation of richly detailed image content. The approach is to train only the generator part of the model and test it with test sets, and the results are shown in the SwinIris column of the table. The complete training process is divided into two phases. During the first phase, only the generator is trained. During the second phase, the adversarial structure is introduced. The results are displayed in the SwinGIris column of the table. The CASIA-Iris-Interval dataset is used to represent a dataset with fewer categories of sharp iris texture details in bright environments. The experimental results are shown in Table 1, and the visual results are displayed in Figure 6.  
Compared with other super-resolution models, SwinGIris obtains the highest score in PI, which indicates that our model effectively recovers the detailed information of iris images and is closer to human visual perception. It can be found that, under subjective \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Bicubic** & **SRCNN** & **ESRGAN** & **SwinIR** & **HAT** & **IrisDnet** & **SwinIris** & **SwinGIris** \\ \hline PSNR & 31.4261 & 31.6137 & 31.9372 & 33.6537 & **34.7421** & 31.5786 & 34.5798 & 33.5663 \\ SSIM & 0.8102 & 0.8063 & 0.8301 & 0.8703 & **0.8927** & 0.7994 & 0.8857 & 0.8639 \\ PI & 8.1907 & 9.1302 & 6.3256 & 7.9390 & 7.8452 & 6.0320 & 7.7257 & **5.9280** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of image quality on CASIA-Iris-Interval (best results are indicated with bold). perception, the images reconstructed by our model have a sharper texture, as shown in Figure 12. Compared to SwinIris, SwinIris exhibits some deficiencies in the restoration of fine details. As shown in Figure 13, in order to show the details of the generated iris image, we intercepted the same area of the image generated by different methods and then zoomed in on the the red box area. Each method recovers images with some loss of detail; however, our method has a stronger ability to recover high frequency information. Moreover, the metrics in PSNR and SSIM are not the best, as the GAN super-resolution model is featured with more substantial generative power and a higher visual effect. The generated images may contain more high-frequency details that may be noisy or distorted for metrics like PSNR and SSIM. Our ablation model, SwinIris, also possesses higher PSNR and SSIM metrics than SwinGIris. Considering the effectiveness of adversarial training in enhancing PI, a fair comparison with HAT should be SwinIris, which is without the second stage of training. SwinIris is doing better than HAT, while for PSNR/SSIM, SwinIris performs only a little worse than HAT. For iris images, high PSNR and SSIM can lead to problems with loss of detail. As shown in Figures 12 and 13, although HAT has the highest PSNR/SSIM, it is not as rich in detail as SwinGIris. Figure 12: Visual results of CASIA-Iris-Interval dataset. Figure 13: Visual details of CASIA-Iris-Interval dataset. To show the details of the generated iris images, we zoomed in on a portion of the iris region. The area in red is the area we intercepted and zoomed in on.