## 2 Principle
### High-Resolution 3D View Image Generation Based on GAN  
Based on the method described in Section 2.1, it is possible to generate a low-resolution view image. In order to achieve efficient generation of super high-resolution 3D images, a refined SRGAN network is proposed. Traditional CNN-based super-resolution reconstruction algorithms usually use mean squared error (MSE) as the loss function, which cannot guarantee the perceptual consistency between the generated images and the real images. SRGAN introduces perceptual loss, which calculates the feature differences between the generated images and the real images using a pre-trained feature extraction network, thereby better preserving the perceptual details of the images. In addition, the discriminator of the SRGAN network adopts a deep convolutional neural network, which can effectively learn the differences between the generated images and the real images. This enables SRGAN to generate more realistic and natural high-resolution images, avoiding the issues of excessive smoothness or over-sharpening. To prevent the issues of gradient diffusion and degradation caused by an increasing number of network layers, the generator network has incorporated a skip-connection residual network structure. Each residual block includes two convolutional layers, followed by batch normalization and leaky rectified linear unit (LReLU) activation function. Furthermore, the visual geometry group (VGG) network is employed to compute the content loss instead of the traditional MSE. All convolutional kernels have a 3 \(\times\) 3 structure with 64 feature maps, aiding in the extraction of richer feature information. We can input a low-resolution image and perform convolution on the convolutional layer with parameters set to 3 \(\times\) 3 \(\times\) 64, meaning there are 64 convolution kernels with a 3 \(\times\) 3 structure and step size of 1. LReLU is used as the activation function, followed by training in 6 residual block networks before entering the convolutional layer. Finally, the image is upscaled by a factor of 2 using the scaling convolution method. The nearest-neighbor interpolation is employed to upscale the image by 2 times, and then it is passed through the convolutional layer. After that, a convolution operation is performed, and a high-resolution image is outputted. This article is based on an improved SRGAN network that removes the BN layer from the previous SRGAN. The BN layer is implemented by normalizing each batch of data in the channel dimension by subtracting the mean of the batch and dividing it by the standard deviation of the feature values of each sample. In this way, the feature values of each layer are in a specific range, which reduces the problem of gradient disappearance during training. However, since the BN layer only considers the statistical properties of the current batch data, the correlation between different batches is ignored. This means that if there are differences in the data distribution between batches, the BN layer may not be able to completely eliminate these differences, introducing an artifact problem. Such artifacts can manifest themselves as unnatural textures or color deviations in the generated images, among others. Moreover, since the BN layer only exploits the statistical properties of the local batch data, it may affect the generalization ability of the network. Because the generalization ability requires the network to be able to adapt to different distributions of data, the normalization process of the BN layer is only carried out on the local batch data, which cannot fully consider the global characteristics of the entire data set. Therefore, removing BN layers can reduce this artifact problem and improve the generalization ability of the network by increasing the correlation between the basic blocks. Such improvement measures help to improve the accuracy of the reconstruction task and the quality of the generated images.