## 2 Methodology
### Lightweight Super-Resolution Network Optimization Based on Channel Attention Mechanism
#### 2.4.1 Edsr-Tiny Network  
The EDSR-Tiny lightweight super-resolution network structure, designed in this paper, is shown in Figure 4. By combining module simplification and the attention mechanism, the entire architecture accomplishes effective and efficient image reconstruction while carrying over the residual learning concept from the old EDSR. Through residual skip connections and sequential connections, the modules are closely coordinated, improving reconstruction accuracy and facilitating an efficient information flow. High-resolution CT sinusoidal graph super-resolution reconstruction jobs can be processed with low computer resources thanks to the network's general design, which balances performance and a lightweight size. **The high-resolution images:**  
The process of obtaining high-resolution images is as follows: Firstly, the threshold segmentation method is used to remove the metal pixels in the CT slice to generate a section that only contains metal artifacts. Then, a sinogram is generated by applying the Radon transform on the filled image. Finally, the two sinograms are differentiated. The differentiated graphs are processed as images, and the inverse Radon transform is performed to obtain high-resolution CT sections. The subsequent high-resolution sinogram was also obtained from this. **The key modules of the network structure:**  
Input layer (Input): This module is in charge of performing the preliminary feature extraction after receiving the preprocessed metal artifact difference sine graph. The original image is converted into a high-dimensional feature space appropriate for deep feature learning by a number of convolution processes. Convolutional layer (Conv): This component improves the image's texture and edge information, further extracts the image's local spatial characteristics, and supplies a rich feature base for the residual block processing that follows. Residual block: The network's central component, the residual block, is made up of several convolutional layers and activation functions and is employed for nonlinear mapping and deep feature extraction. The residual structure enhances data transfer efficiency and mitigates the issue of vanishing gradients in deep network training via skip links. In particular, the residual block ensures smooth information flow and nonlinear expression capabilities by containing two ReLU activations and three convolution processes. Figure 4: EDSR-Tiny network. Including the entire framework of the network and the network structure of key modules. Channel attention layer (CALayer): A module for the channel attention mechanism built within the residual block. It increases the sensitivity and recovery capacity of the network for significant picture details by adaptively modifying the feature weights of each channel, which highlights the critical channel information and reduces redundant noise. Upsample layer: This module recovers the spatial scale of the high-resolution image, gets ready for the final image output, and enlarges the depth feature map to the target resolution using methods like interpolation or transpose convolution. **The formula description of the network structure:**  
Assume that the input low-resolution sinogram is \(X\) and that the network produces a high-resolution reconstruction result of \(\hat{Y}=F(X;\theta)\). The mapping function of the entire super-resolution network is denoted by \(F\), with \(\theta\) as its parameter. Initial feature extraction layer: Extract the initial features through convolution operations:  
\[F=Conv(X) \tag{4}\]  
Representation of residual blocks: There is a single residual block in the network. The convolution and ReLU activation layers are used to nonlinearly map the features within each residual block, and a channel attention mechanism (CALayer) is introduced to modify the channel weights.