## 2 Principle
### High-Resolution 3D View Image Generation Based on GAN
The residual network layers are added together to form a dense residual network, allowing for more connections between each residual block, as shown in Figure 3.  
We use relatively few basic blocks to compose the generative network, which can reduce the training time and the time required to generate high-resolution images. The GAN uses content loss and adversarial loss to improve the realism of the output image and generate high-frequency information. The loss function is expressed as follows:  
\[l^{SR}=l^{SR}_{X}+10^{-3}l^{SR}_{Gen} \tag{5}\]  
where \(l^{SR}\) is the adversarial loss, which is the loss incurred when the discriminator identifies the generated images from the generator as natural images. The content loss is divided into two parts: \(l^{SR}_{X}\), which is the MSE loss of the generator network, and \(l^{SR}_{Gen}\), which is the feature loss obtained by inputting the generated images into the VGG network. The weight of the adversarial loss is \(\text{l}=10^{-3}\). The MSE loss is a commonly used loss function to measure the difference between the high-resolution images generated by the generative network and the real images. If the loss function only consists of the MSE loss, it can be represented as:  
\[l^{SR}_{MSE}=\frac{1}{r^{2}WH}\sum_{X=1}^{rW}\sum_{Y=1}^{rH}\left(l^{HR}_{X,Y}- G_{\theta}(l^{LR}_{X,Y})\right) \tag{6}\]  
where \(W\) and \(H\) represent the width and height of the image, \(r\) is the magnification factor for super-resolution, \(l^{HR}_{X,Y}\) is the pixel value of the original image \((x,y)\), and \(G_{\theta}(l^{LR}_{X,Y})\) represents the reconstructed image output by the generative network. The VGG loss function utilizes the feature vectors from a pre-trained VGG-19 network to measure the perceptual similarity between the generated image and the original image. This loss is calculated by comparing  
Figure 3: The architecture of the proposed networks: (a) generative network and (b) dense block. the feature values produced by the VGG network for both images. The VGG loss can be expressed as:  
\[l_{VGG}^{SR}=\frac{1}{r^{2}WH}\sum_{X=1}^{rW}\sum_{Y=1}^{rH}{(G_{VGG}^{HR}(i,j)(I_ {X,Y}^{HR})-G_{VGG(i,j)}(I_{X,Y}^{LR}))}^{2} \tag{7}\]  
where \(i,j\) represent the feature maps output after the \(j_{th}\) convolutional layer and before the \(i_{th}\) max pooling layer. It is the feature value obtained through the VGG network. The purpose of the generative network is to generate realistic images to deceive the discriminative network and produce high-quality images. Therefore, the adversarial loss is used to measure the similarity between the generated image and the real image. The adversarial loss is:  
\[l_{Gem}^{SR}=\sum_{n=1}^{N}{\log(1-D[G_{\theta}(I_{X,Y}^{LR})])} \tag{8}\]  
where \(D[G_{\theta}(I_{X,Y}^{LR})]\) is the probability that the discriminator classifies the generated image by the generator as a real image. The VOC2012 dataset was used to train the network, which contains a large number of high-quality images and has wide applicability, making it suitable for SR training [48]. The training dataset consists of images with varying complexity captured using different sets of lenses, enabling the network to have better generalization ability. To expand the dataset, the images in the dataset can be cropped, rotated, mirrored, etc., to increase the number of samples. During training, the high-resolution images were first down-sampled using bicubic interpolation to obtain low-resolution images which were then upscaled and compared with the original images to train the network parameters. To generate low-resolution and high-resolution image pairs for training, we first cropped all images in the dataset to 88 \(\times\) 88 images, then downsampled to the corresponding low-resolution image. The adaptive moment estimation (Adam) was used with a learning rate of \(10^{-4}\) for gradient descent during training. The network was trained for 100 epochs with a batch size of 16.