## 3 The Proposed Network Model
### Generator Structure  
The generator structure designed in this paper is shown in Figure 6. Here, we adopt a dual convolution operation; a feature mapping attention mechanism is introduced to extract the important feature information of the low-resolution image and transfer it between channels on one path, whereas an EDSR deep residual convolution is introduced directly to extract the comprehensive feature information of the image on the other path. The image information extracted from the comprehensive feature is then fused together with the important feature information in the reconstruction layer to restore the image closer to the perceived quality of human eyes. The extracted important feature information, which serves as a refining element, makes up for the deficiency of deep residual network feature extraction.  
The channel attention mechanism not only plays a significant role in improving the reconstruction accuracy of the network, but also has high module portability, which can be easily loaded into the existing network structure. It distinguishes the significance of image channels by giving different weights to image channels through learning. This  
Figure 5: The overall framework of the proposed network.  
Figure 6: The architecture of the generator network.  
allows the network to pay more attention to the rich information channels and increases the feature extraction ability of the network that is also instructive for image reconstruction tasks. Therefore, this article utilizes the channel attention mechanism for image super-resolution reconstruction (SRCAN) [17,18] As shown in Figure 7, the network is set as five submodules, where each 3 \(\times\) 3 convolution operation is followed by an attention mechanism submodule. In addition, the number of modules can be constantly adjusted according to the reconstruction effect.  
The enhanced deep convolutional network module includes five submodules. Each submodule contains three convolution operations and introduces local residuals. Before the information flow enters each submodule, we utilize a \(1\times 1\) convolutional operation to the output of the previous level to perform fusion compression. As shown in Figure 8, local residual learning and global residual learning are conducted within the network. In terms of the scope, local refers to residual learning within the submodule, and global refers to residual learning between the input and output of the entire network.  
In addition, the traditional activation function has a drawback that needs to be modified for achieving better results. Although the ReLU activation function has high calculation efficiency and can make the network converge quickly, the traditional ReLU function leads to the death of neurons. When the input is close to 0 or in a negative interval, the gradient disappears, and the network is unable to carry out the backpropagation of the gradient, resulting in the network being unable to carry out normal learning. This can be expressed as follows:  
\[f(y)=\begin{cases}y&y\geq 0\\ 0&y<0\end{cases}. \tag{5}\]  
In the situation where \(y\) is less than 0, the ReLU activation function leads to the problem of neuron death. Through our research to prevent this from happening, we found that the Leaky ReLU activation function has a nonzero positive slope in the negative axis interval, which is capable of carrying out backpropagation by implementing the property of the ReLU function while preventing the above issue. This can be expressed as follows:  
\[f(y)=\begin{cases}y&y\geq 0\\ \delta y&y<0\end{cases}, \tag{6}\]  
where \(\delta\) is defined as a real number within an interval (0, 1), and the output of the function is ensured to preserve its sample information within this negative input region.  
Figure 8: The architecture of the EDSR module.  
Figure 7: The architecture of the SRCAN module.