## 1 Introduction
The super-resolution generative adversarial network (SRGAN) applies the concept of generative adversarial networks (GANs) to the field of image generation for producing high-resolution photo-realistic images [40]. Compared to traditional methods, they are better at capturing complex patterns and creating intricate textures. Similarly, an enhanced super-resolution generative adversarial network (ESRGAN) was proposed, which utilizes GANs to generate realistic high-resolution images with improved detail and sharpness [41]. Computational efficiency is also a concern in image SR, and the fast super-resolution convolutional neural network (FSRCNN) simplifies the network structure [42], achieving faster speeds compared to each camera viewpoint independent rendering (ECVIR) [43]. Another approach [44] simplifies the generation network to only consist of two convolutions, an activation function, and a deconvolution. While this accelerates computation, designing the generation network too simply can limit its ability to capture texture details. The traditional SRGAN network utilizes convolutional layers with batch normalization (BN) to extract feature maps during training. BN normalizes the data along the channel dimension for each batch, ensuring that the values of each layer are within a specific range. This helps alleviate the problem of vanishing gradients during the training process. However, BN also introduces additional computational complexity and memory usage. Furthermore, BN only considers the statistical characteristics of the internal batch data during normalization and ignores the inter-batch correlations, which can lead to artifacts and impact the generalization ability of the network [45]. Therefore, in this study, we chose to remove the BN layers to reduce computational complexity and improve the network's generalization ability. Additionally, addition and subtraction operations are added between the basic blocks to increase the inter-block correlation and enhance the reconstruction effect. This paper proposes a method for CIIR based on GAN super-resolution. First, a low-resolution reconstructed 3D image based on pixel mapping is generated, which allows for obtaining a view image in less time, and then a lite GAN is employed to super-resolve the low-resolution 3D image. Eliminating the BN layer from SRGAN leads to improvements in generalization and increased high-frequency texture details while reducing computational complexity and memory usage. The residual network layers are then added together to form a dense residual network, which allows for greater connectivity between each residual block. This approach ensures that there is more communication between each residual block and improves the overall performance of the network. After the process of the lite GAN network, super-high resolution reconstructed 3D images are obtained. The experimental results show that the proposed method achieves SSIM above 0.95 in different 3D models and reduces network training time by about 20%. This article is structured as follows: Section 2 introduces the fundamental principles of the algorithm. Section 2.1 provides an overview of ray tracing and pixel mapping, explaining how to generate low-resolution images from elemental image arrays. Section 2.2 focuses on high-resolution 3D image generation based on GAN, explaining how to enhance the effect of the reconstructed image. Section 3 presents the experimental results and engages in comprehensive discussions. Finally, Section 4 draws conclusions based on the research conducted.