# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 4 Experimental Results
### Training Configurations and Evaluation Metrics
Bicubic interpolation restores the values of the surrounding 16 pixels using a 2D equation, resulting in a smoother image but lacking \begin{table}
\begin{tabular}{c c} \hline
**Component** & **Specification** \\ \hline GPU & NVIDIA RTX 3090 (24 GB VRAM) \\ CPU & AMD Ryzen 3950X (16-core, 32-thread) \\ RAM & 64 GB DDR4 \\ Operating System & Ubuntu 20.04 LTS \\ Framework & PyTorch \\ \hline \end{tabular}
\end{table} 
Table 2: Hardware and software configuration. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Optimizer & Adam \\ Loss Function & L1 Loss \\ Batch Size & 64 \\ Epochs & 100 \\ Input Patch Size & 48 \(\times\) 48 \\ Upscaling Factors & 2 \(\times\) (48 \(\times\) 48 \(\rightarrow\) 96 \(\times\) 96), 3\(\times\) (48 \(\times\) 48 \(\rightarrow\) 144 \(\times\) 144), 4 \(\times\) (48 \(\times\) 48 \(\rightarrow\) 192 \(\times\) 192) \\ \hline \end{tabular}
\end{table} 
Table 3: Training parameters. high-frequency information, leading to blurry results in regions with similar patterns, such as dark lesions. VDSR, which uses 20 convolution layers and residual learning, improves the performance of edge preservation compared to bicubic interpolation. EDSR, with its deeper architecture comprising 69 convolution layers, produces sharper results than VDSR. The proposed CSBlock and attention-block methods handled high-frequency information better than existing networks. However, the numerical values were similar to those of EDSR. Nonetheless, the EDSR structure, which mainly consists of convolution layers, lacks emphasis on specific high-frequency features. In contrast, our proposed method optimizes the network, allowing for improved performance in specific high-frequency regions and faster learning. Table 4 displays the PSNR and SSIM results for the ISIC dataset of 3371618 images. Owing to the complex and similarly colored patterns, our proposed method demonstrates improved results with higher values and more high-frequency components than the existing algorithms. Figure 14: ISIC_3371618 result: (**a**) original image, (**b**) bicubic interpolation image, (**c**) VDSR, (**d**) EDSR, and (**e**) the proposed method. Figure 15 presents the super-resolution results for 4382016 images from the ISIC 2020 challenge dataset. In cases with straight patterns, such as fur, bicubic interpolation results in staircase artifacts and blurry edges. VDSR produces improved results compared to bicubic interpolation but still exhibits blurriness. EDSR enhances the sharpness of the fur area but introduces staircase artifacts and unnatural regions. The proposed method demonstrates improved results in addressing these issues. Table 5 displays the PSNR and SSIM results for ISIC_4382016. The results are improved compared to existing methods in addressing straight patterns, like fur and skin textures. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Scale**} & **Bicubic** & **VDSR** & **EDSR** & **Proposed Method** \\  & & **PNSR/SSIM** & **PNSR/SSIM** & **PNSR/SSIM** & **PNSR/SSIM** \\ \hline \multirow{3}{*}{ISIC\_3371618} & \(\times\)2 & 36.4989/0.9862 & 37.2781/0.9873 & 38.9535/0.9856 & 39.6759/0.9930 \\  & \(\times\)3 & 33.0617/0.9734 & 35.5203/0.9776 & 36.0538/0.9718 & 37.2398/0.9801 \\  & \(\times\)4 & 31.4003/0.9645 & 31.4003/0.9661 & 33.6596/0.9633 & 34.8293/0.9763 \\ \hline \end{tabular}
\end{table} 
Table 4: ISIC_3371618 PSNR/SSIM for scale factors \(\times\)2, \(\times\)3, and \(\times\)4. Figure 15: ISIC_4382016 results: (**a**) original image, (**b**) bicubic interpolation image, (**c**) VDSR, (**d**) EDSR, and (**e**) the proposed method.