# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 3 Proposed Method
### CSBlock
This enables the extraction of representative feature values for each channel. GAP helped to obtain representative feature maps containing channel-specific information without requiring numerous computations or parameters. GAP reduced the number of parameters and computational complexity by calculating the average instead of processing every pixel value in the feature map. Consequently, GAP is leveraged to generate representative feature maps that consider interchannel relationships, thereby facilitating efficient feature extraction. \[s(x)=f(W_{d}\times\delta(W_{d/4}\times H_{GP}(x))). \tag{7}\]  
Equation (7) presents the calculation of the interchannel correlation and importance based on the GAP result, \(H_{\text{GP}}(*)\), obtained from Equation (6). Here, f and \(\delta\) denote the Sigmoid and ReLU activation functions, respectively, while \(W_{d}\) and \(W_{d/4}\) represent the weights of the fully connected layers with output feature map channel sizes of \(d\) and \(d/4\), respectively. We computed the interchannel correlation and its importance using two fully connected layers. The results were transformed into attention values ranging from 0 to 1 using a Sigmoid activation function. The attention values obtained indicate the importance of each channel in the feature map. Furthermore, by employing a fully connected layer that reduces the number of channels in the middle, we effectively reduced the computational complexity and number of parameters. This simultaneously enhanced the computational efficiency while accurately inferring the interchannel correlation and importance. This design enables the efficient computation of channel attention. \[CA(x)=x\times s. \tag{8}\]  
Equation (8) generates an emphasis feature map through multiplication operation of the input feature map on the channel attention feature map obtained from Equation (7). Here, x represents the input feature map, and s represents the channel attention feature map obtained from Equation (7). This process adjusts each channel of the input feature map by emphasizing high-frequency features and suppressing irrelevant ones. The channel attention feature map output values of 0-1, through a Sigmoid activation function, indicate the level of importance for each channel. The necessary channels are preserved by multiplying them with the input feature map. However, irrelevant channels are suppressed with values closer to 0. This enables feature refinement by emphasizing specific regions of the input feature map based on interchannel importance while inhibiting less important areas. The channel attention map considers interchannel importance, allowing for the accentuation of relevant parts and suppression of less significant areas, thereby enabling effective feature extraction. This provides a clear understanding and can be applied to the input feature maps for more efficient feature extraction. Figure 11 illustrates the spatial attention structure of the proposed method. In contrast to the conventional CBAM, this structure does not undergo a pooling process, but instead forms a convolution output as a single-channel representation. This minimizes the loss of spatial information and emphasizes spatial features. In the conventional CBAM, the pooling process reduced spatial information in some instances. However, in the proposed method, we omitted the pooling process and used convolution to reduce information loss while emphasizing the spatial features. To provide a comprehensive view of the convolutional self-attention block (CSBlock), Figures 9-11 have been consolidated into an integrated representation, as shown in Figure 12. This unified diagram illustrates the sequential processes of channel attention and spatial attention within the CSBlock, emphasizing how these mechanisms complement each other to enhance high-frequency features for efficient image analysis and processing. This structural modification facilitates a more accurate extraction of spatial features and provides comprehensible explanations.