## 2 The Related Work  
In this section, we describe the details of the SRGAN network architecture, a model that utilizes adversarial training thinking during the image super-resolution task to provide more abundant image details and improved image authenticity in the output. The model is divided into two sub-models, namely, G (generator) and D (discriminator). The overall framework of the SRGAN algorithm is shown in Figure 1, where the G network takes low-resolution images as input, and then generates targeted output that is close to ground-truth images. This basically describes the entire process of the image super-resolution reconstruction task. Next, the D network is used to assess the main task output for achievingrefined results by making judgement on a true or false basis. The reconstructed image of the G network is compared with the corresponding ground-truth image. Through this confrontation training, the reconstruction effect of the model is constantly improved. The SRGAN model is different from that of the traditional GAN. The GAN is random noise, whereas the SRGAN is a low-resolution image. The generator network structure is shown in Figure 2; the core structure of the network is a residual module composed of two 3 \(\times\) 3 convolution layers, a standard regularization (BN) layer [14], and a ReLU activation function. At the end of the network, a sub-pixel convolution layer is used to up-sample the image to obtain a reconstructed image of identical size to the original image. The residual module structure is shown in Figure 3.  
The core module of the discriminant network in the SRGAN model is different from the core of the generator network. The core module is composed of a convolution layer, a Leaky ReLU activation function, and a standard regularization BN layer. A diagram of the discriminator is shown in Figure 4; after the discriminator receives image information as its input, it passes it through its Sigmoid function, which a makes judgement and classifies the results into true or false categories. Figure 1: The frame diagram of SRGAN. Figure 3: Residual module structure. Figure 2: Generator network structure. In order to improve the final image generation effect in the SRGAN network, the composition of the loss function is changed, and the concept of perceptual loss [15] is introduced. In the original SRGAN, loss is defined as the sum of different weights of content loss and adversarial loss, as shown in Equation (1), where \(L_{x}^{SR}\) is the content loss, \(L_{Gen}^{SR}\) is the adversarial loss, and \(L_{i}^{SR}\) is the final fusion loss. \[L^{SR}=L_{x}^{SR}+10^{-3}L_{Gen}^{SR}. \tag{1}\]  
The definition of the pixel-by-pixel MSE loss function is shown in Equation (2). \[L_{MSE}^{SR}=\frac{1}{r^{2}HW}\sum_{x=1}^{TH}\sum_{y=1}^{PW}\left(I_{x,y}^{HR}- G_{\theta_{G}}(I^{LR})_{x,y}\right)^{2}, \tag{2}\]  
where \(H\) and \(W\) represent the size of the matrix, \(r\) represents the up-sample scale, and \(I^{HR}\) and \(I^{LR}\) are the high-resolution and low-resolution images, respectively. The MSE loss function is often used as the convergence target in the field of super-resolution reconstruction; in particular, in the SRGAN model, a concept of loss close to human perception is proposed. More specifically, this concept is defined by the direct Euclidean distance between the \(G_{\theta_{G}}(I^{LR})\) and \(I^{HR}\) features of the super-resolution image extracted by VGG [16], and the loss function of VGG is defined as follows:  
\[L_{\text{VGG}/i,j}^{SR}=\frac{1}{H_{ij}W_{i,j}}\sum_{x=1}^{H_{ij}}\sum_{y=1}^{ W_{i,j}}\left(\phi_{ij}(I^{HR})_{x,y}-\phi_{ij}(G_{\theta_{G}}(I^{LR}))_{x,y} \right)^{2}. \tag{3}\]  
\(H_{i,j}\) and \(W_{i,j}\) represent the dimension values of the feature map in the feature extraction network. In the VGG feature extraction network, \(\phi_{ij,j}\) represents the characteristic diagram before the \(i\)th largest pooling layer and after the activation of the \(j\)th convolution layer.