# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 3 Proposed Method
### CSBlock  
The CSBlock is effective for emphasizing high-frequency features in image analysis and processing tasks, and can achieve high accuracy and performance. Conventional convolution operations extract features by measuring the pattern similarity between the center and surrounding pixel values. However, this method does not effectively extract the patterns of pixels that exceed the kernel size, which is particularly striking for high-frequency features, and obstructs accurate image analysis. The CSBlock is designed to address this issue by combining channel and spatial attention with an input feature map to emphasize high-frequency features. Channel attention emphasizes necessary features by calculating the correlation and importance between channels of the input feature map. However, spatial attention emphasizes spatial features by calculating the correlation and importance among pixels. Thus, a CSBlock can effectively extract high-frequency features and achieve accurate image analysis results using this approach. Figure 8: Attention block structure. Figure 9 illustrates a CSBlock, which combines channel and spatial attention. This structure progresses through the following steps:  
Step 1: The input feature map is reduced using global average pooling (GAP), and the representative feature values of each channel are obtained by averaging the input feature map. Step 2: The reduced feature map is used with fully connected layers to calculate the correlation and importance between channels, allowing us to grasp the importance of each channel and conduct channel attention by multiplying it by the input feature map. Step 3: A feature map is generated by calculating the correlation between pixels using the convolution of one channel. This process extracted features by considering the spatial patterns of the input feature map. Step 4: Spatial attention is performed by multiplying the feature map generated by calculating the correlation between pixels with the feature map on which channel attention is performed. This method emphasizes the spatial features of the input feature map. Through this combined structure of channel and spatial attention, the CSBlock emphasizes high-frequency features and enables efficient image analysis and processing with fewer parameters and operations. This structure helps us clearly understand the core functions of the CSBlock and apply them to the analysis. Figure 10 presents the channel attention structure of the proposed method. In contrast to the conventional CBAM, we removed the max pooling component and used the average pooling. This architectural adjustment reduced the emphasis on high-frequency features while achieving efficient channel attention with lower computational requirements. The previous CBAM accentuated high- and low-frequency features by incorporating max pooling and average pooling. However, this approach often resulted in an excessive emphasis on high-frequency features, which hindered the accurate extraction of the desired characteristics. Hence, in the proposed structure, we eliminated the max pooling component to mitigate the emphasis on high-frequency features, relying solely on the average pooling. Consequently, this modification helped enhance computational efficiency while effectively conducting channel attention. This facilitated the accentuation  
Figure 10: Channel attention structure. Figure 9: Channel spatial block. of relevant features and improved the ability to discern patterns within the input images. Such structural modifications contribute to better comprehension and yield superior image analysis results. \[H_{\text{GP}}(x)=\frac{1}{H\times W}\sum\
olimits_{i=1}^{H}\sum\
olimits_{j=1}^ {W}x(i,j). \tag{6}\]  
Equation (6) represents the GAP process, which is used to perform channel attention. H and W denote the height and width of the feature map, respectively, whereas \(H_{\text{GP}}\) (*) represents the step of obtaining a representative value for each channel in the feature map by computing the average of all pixel values within that channel.