## 3 Experimental Results  
In the experiments, the PC hardware configuration consists of Intel(R) Core(TM) i7-11,800H CPU @ 2.30 GHz with 16 GB RAM and NVIDIA GeForce RTX 3060 laptop GPU. The parameters of the ray tracing are shown in Table 1.  
The test objects encompass a simple textured dolphin, a complex textured warcraft, and a hamburger. Figure 4 shows the elemental image array generated by ray tracing, as well as upper, lower, left, and right view images resulting from pixel mapping. \begin{table}
\begin{tabular}{c c} \hline \hline Array size & 50 \(\times\) 50 \\ \hline Pitch & 7 mm \\ \hline Distance between 3D object & \\ and camera array & 200 mm \\ \hline Focal length & 28.58 mm \\ \hline View angle & \(10^{\circ}\) \\ \hline Resolution & 8 \(\times\) 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the ray tracing. In order to quickly obtain high-resolution 3D images, light generative networks and deep discriminative networks can be used to achieve better results in image super-resolution, while reducing the time required for the generation network to produce high-resolution images. While SRGAN uses 16 basic blocks to increase the complexity of the network, this paper uses fewer basic blocks to improve training speed while reducing complexity, as shown in Table 2.  
Table 2 shows that the structural similarity index measure (SSIM) and peak signal to noise ratio (PSNR) obtained using 16 basic blocks in SRGAN are not significantly different from those obtained using 6 basic blocks, but the training time is greatly reduced by about 20%. On the other hand, the training time difference between 6 basic blocks and 3 basic blocks is not significant, yet the SSIM and PSNR values are much lower than those obtained using 16 basic blocks. This indicates that while maintaining the same image output quality, appropriately reducing the number of basic blocks can lead to faster training of network parameters. To enhance the training speed while ensuring that the image reconstruction quality remains unaffected, a comparison was also made between the traditional SRGAN and the proposed method. The training results of traditional SRGAN showed a PSNR of 23.9041 dB and an SSIM of 0.7091, while the training results of the proposed method showed a PSNR of 23.8863 dB and an SSIM of 0.7102. It can be observed that there is not a significant difference in the training results between the two methods. This indicates that the proposed method can train the network quickly without compromising on the image reconstruction quality. In summary, the proposed method in this paper offers both time efficiency and quality preservation. Due to the limited number of cameras, through pixel mapping, we can only reconstruct the view images with lower resolution. Therefore, super-resolution is used to improve these reconstructed images; the results are shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Basic Blocks** \\ \end{tabular} & \
\begin{tabular}{c} **Training Time** \\ \end{tabular} &
\begin{tabular}{c} **Image** \\ **Generation Time** \\ \end{tabular} & **SSIM** & **PSNR** \\ \hline
3 & 5 min 48 s & 1.03 s & 0.718 & 22.6 \\
6 & 6 min 37 s & 1.065 s & 0.720 & 22.9 \\
16 & 8 min 20 s & 1.09 s & 0.722 & 23.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of basic blocks and training time. Figure 4: (a) 3D model dolphin (b) elemental image array (c) view images. To verify the feasibility of the proposed method, a comparison of reconstructed 3D images is made between our method, SRCNN [37], bicubic interpolation [37], and pixel mapping [13]. For simplicity, we select a specific viewpoint for super-resolution reconstruction. From the local zoom-in effect, it can be observed that the proposed method provides reconstructed images with sharper texture details and better subjective image quality than others.