## 3 Experimental Results
In addition, SSIM is used as an objective quantitative metric for the reconstructed image quality evaluation, as shown in Table 2.  
Figure 5: Reconstructed images using (**a**) proposed method, (**b**) SRCNN, (**c**) bicubic interpolation, and (**d**) pixel mapping. From Figure 5 and Tables 2 and 3, it can be observed that the proposed method has better view effects for the three different complexity 3D models. It also exhibits better generalization ability, displays more high-frequency information, and ensures a better 3D imaging effect as perceived by the human eye. The proposed method not only achieves improvements in SSIM but also reduces the training time by about 20%. In summary, using the improved light GAN network can quickly reconstruct the low-resolution view images at high resolution and improve the 3D image display quality, so as to better represent the details and texture information. By removing the BN layer, better extraction of high-frequency information from images can be achieved, as the BN layer may introduce unnecessary smoothing to high-frequency details in the images. Removing the BN layer allows the network to preserve image details and texture features more effectively. Furthermore, reducing the number of basic blocks brings two benefits. Firstly, it reduces the number of parameters and computational load, thereby accelerating network training and high-resolution image generation. Secondly, reducing the number of basic blocks prevents the network from becoming overly complex and helps avoid overfitting issues, resulting in generated images with better generalization ability. Increasing intra-block operations enhances connectivity between layers, allowing the network to better propagate and utilize information. This improves the network's ability to capture image details, leading to the generation of more realistic and clear high-resolution images. The VOC2012 dataset was used for training, which includes a large number of scene images covering various types of images such as people, landscapes, etc. The dataset was augmented using techniques such as rotation and scaling to increase its diversity, which improves the model's ability to generalize to different scenes and enhances its performance. Although the training time is long, once the model is trained, the subsequent super-resolution can be applied directly without the need for further training. It only takes about 1 s to super-resolve a new input image using the trained model, as shown in Table 2. Therefore, it is feasible to use super-resolution for computational integral imaging reconstruction. Reference [44] also indicates that using super-resolution methods can improve the reconstruction speed of integral imaging. To verify the feasibility of the proposed method, three models with different complexities were evaluated, and image quality evaluation metrics such as PSNR and SSIM were used for comparison. The results showed that the proposed method exhibited higher PSNR and SSIM values while reducing training time. This means that the proposed method can better preserve image details, improve image fidelity, and significantly enhance image reconstruction quality. Through extensive testing on a large amount of data, we confirmed that the performance difference of the proposed method is significant and not just due to random errors. Therefore, we can conclude that the proposed improvement method has statistical significance and is effectively reflected in the image quality evaluation metrics. It should be noted that the proposed method performs well in SR for images with relatively high resolutions. However, if the resolution of the view images is too small, the method may suffer from distortion issues. In particular, when dealing with images of size 25 \(\times\) 25 pixels, the performance is not ideal. While the current performance may not be ideal for such low-resolution images, ongoing research and advancements in SR algorithms hold promise for further enhancing the method's effectiveness in handling