## 3 The Proposed Network Model
### Information Extraction Layer  
Information enters the information extraction module. For RGB images, the red channel parameters of the image are taken as important information, and the attention mechanism module is introduced. [PERSON] et al. [11] first proposed the concept of channel attention mechanism in 2018, explicitly modeling the interdependence between feature channels, learning to obtain the importance of each feature channel, and then promoting useful features according to their importance levels, and suppressing features that are not used for the current task. In this way the feature expression ability of the network can be significantly improved. In related studies, the attention mechanism has been widely used for deep learning in various application fields, including natural language processing [12; 13], image segmentation [14], fog computing [15] and machine learning [16; 17]. These models take advantage of the selective attention mechanism of human vision [18], quickly select target areas to browse global information, and guide more attention to suppress other less useful information. It is to our advantage that channel attention mechanism module can be easily added to the existing network structure, to improve the accuracy of the original network in image classification and scene classification tasks. Specifically, the channel attention mechanism distinguishes the importance of image channels by learning to give different weights to image channels, so that the network pays more attention to channels containing rich information, and thus enhancing the feature extraction ability of the network, which is also enlightening for image super-resolution tasks. Therefore, this paper applies the channel attention mechanism to support the process of image super-resolution reconstruction. [PERSON] et al. [19] first applied the channel attention mechanism to the field of image super-resolution reconstruction, proving the high effectiveness of the channel attention mechanism for image super-resolution tasks. As shown in Figure 6, let \(X=[x_{1},x_{2},\ldots,x_{c},\ldots,x_{C}]\) as input, Where \(C\) stands for the characteristic graphs of size \(H\times W\), and the channel statistic \(z\in R^{c}\) can be obtained by contracting \(X\). Then the calculation of the \(c\)th \(z\) is as the following:  
\[z_{c}=H_{GP}(x_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}x_{c}(i,j) \tag{8}\]  
where, \(x_{c}(i,j)\) represents the value of the \(c\)th feature \(x_{c}\) at the position \((i,j)\), \(H_{GP}(\cdot)\) represents the global average pooling function, then we use the scaling mechanism to adjust the image features:  
\[s=f(W_{2}\delta(W_{1}z)) \tag{9}\]  
\[\hat{x}_{c}=s_{c}\cdot x_{c} \tag{10}\]  
In order to increase the nonlinearity of the network, two full connection layers instead of one are selected to form the bottleneck layer to flexibly capture the nonlinear interaction relationship between channels. As shown in Formula (9), \(W_{1}\) represents channel dimension reduction operation, and then \(W_{2}\) represents channel dimension upgrading operation, The correlation between feature channels is displayed by parameter \(W\), and generates weight \(s\) for each feature channel. Among them, \(s_{c}\) and \(x_{c}\) represent the scaling factor and characteristic map of the \(c\)th channel respectively and listed as Formulas (8)-(10). The final output of the channel attention mechanism can be obtained as \(\hat{x}_{CA}=[\hat{x}_{1},\hat{x}_{2},\ldots,\hat{x}_{c},\ldots,\hat{x}_{C}]\)  
Figure 6: Feature map attention mechanism. In IR, as shown in Figure 7, a set connection module is added. Each module is composed of several diversity dense connection blocks, local feature fusion and local residual learning. The dense connection module refers to the connection mode in Densenet [20], which creates multiple connection blocks by changing the size, arrangement and proportion of each convolution core in the module.