# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 3 Proposed Method
### Upsampling  
The conventional approach to image super-resolution typically involves deconvolution to expand the feature map. However, this method often introduces checkerboard artifacts, which appear as grid-like patterns in reconstructed images. These artifacts are primarily caused by an uneven overlap of convolutional kernels during the deconvolution process. Such artifacts can significantly degrade image quality, particularly in tasks where high precision is required, such as medical imaging. The impact of checkerboard artifacts on super-resolution tasks has been extensively discussed in the literature [32].  
To address this issue, the proposed CS-SISR model utilizes a subpixel convolution technique. Subpixel convolution rearranges the feature map to expand its resolution without introducing artifacts, effectively eliminating checkerboard patterns. This approach minimizes redundant operations and provides an efficient method for feature map expansion while maintaining high image quality. Super-resolution involves restoring a high-resolution image from its low-resolution counterpart. Traditional methods rely on convolution filters or multiple convolution operations to extract features. However, these methods often face challenges, such as increased network complexity and a high number of parameters. Subpixel convolution resolves these issues by rearranging multiple feature maps to generate an expanded feature map, which increases the redundancy of low-resolution patches. This process ensures efficient feature extraction and expansion while preserving image quality. In the proposed CS-SISR model, subpixel convolution is used to expand feature maps and generate RGB super-resolution images. A combination of residual blocks and three-channel convolutions is employed to enhance high-frequency details, further improving the quality of the reconstructed images.  
Figure 13 presents images of the expanded feature map at each step of the feature map expansion process.  
\[F_{up}=Up([F_{s}(H(I_{lr}),\ F_{D}(H(I_{lr}))]). \tag{11}\]  
Equation (11) represents the formula for the feature map expansion. \(F_{up}\) denotes the expanded feature map, UP represents the subpixel convolution, \(F_{s}\) represents the feature map with a depth of 3, and \(F_{D}\) represents the feature map with a depth of 10. \(H\) represents the convolution, and \(H(I_{lr})\) represents the low-resolution image. Using \(F_{s}(H(I_{lr}))\), \(F_{D}(H(I_{lr}))\) and \(H(I_{lr})\) are obtained by performing convolution operations using \(H(I_{lr})\) as the input. The subpixel convolution generates the expanded feature map, \(F_{up}\), using the low-resolution image, \(H(I_{lr})\), to expand the feature map. The use of subpixel convolution varies according to the scale factor to minimize the parameters, loss during expansion, and computational complexity. For 2\(\times\) and 3\(\times\) expansion, a subpixel convolution is used to expand the feature map. For a 4\(\times\) expansion, a subpixel convolution is applied twice to expand the feature map. Adjusting the subpixel convolution according to different scale factors allows for the efficient execution of the expansion. This method optimizes the parameter usage, reduces the computational complexity, and maintains the quality of the expanded feature map.