## 4 Experimental Results
### Comparison with Classical SR Methods  
Our DMFN was trained using DIV2K [34].Therefore, we compared it with other classical SR methods trained using natural images, such as SRCNN [2], FSRCNN [5], VDSR [8], DRCN [9], LapSRN [7], SRDenseNet [13], DDSR [10], EDSR [14], SRMD [41], SRMDNF [41], SRFBN-S [21] and FAWDN [22]. Moreover, we compared our DMFN+ with FAWDN+ [22], which was trained using the images of MRIMP and part of DIV2K [34], as the network suffered from overfitting when trained with MRIMP. Our DMFN+ was trained with MRIMP, and there was no overfitting, which demonstrates the stability of our method. As illustrated in Table 6, we compared their PSNR and SSIM values, and our DMFN was shown to perform better than the other natural image SR methods with fewer parameters. Furthermore, our DMFN+ demonstrated a better performance than the medical image SR methods with fewer parameters. Finally, a visual comparison of SR medical images was performed, as shown in Figure 10. For ADNI100 [36], the performance of DMFN+ was the best, followed by EDSR [14]. For OASIS100 [37], the performance of DMFN+ was the best, followed by DMFN. However, DMFN+ requires fewer than half the parameters of EDSR [14], so our DMFN and DMFN+ provide a better trade-off. In summary, our methods recover image details and textures better than most of the other methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Params**} & **MRI13** & **ADNI100** & **OASIS100** \\ \cline{3-5}  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Bicubic & - & 37.95/0.9677 & 30.74/0.8943 & 33.54/0.9585 \\ SRCNN [2] & 8 K & 40.76/0.9820 & 32.49/0.9258 & 35.52/0.9717 \\ FSRCNN [5] & 13 K & 40.90/0.9809 & 40.90/0.9809 & 34.47/0.7778 \\ VDSR [8] & 666 K & 41.78/0.9835 & 33.09/0.9328 & 36.70/0.9759 \\ DRCN [9] & 1774 K & 42.15/0.9838 & 33.22/0.9336 & 37.19/0.9779 \\ LapSRN [7] & 251 K & 41.99/0.9840 & 32.96/0.9317 & 36.98/0.9772 \\ SRDenseNet [13] & 7160 K & 42.97/0.9846 & 33.33/0.9348 & 37.69/0.9788 \\ DDSR [12] & 2020 K & 41.92/0.9833 & 33.00/0.9318 & 36.97/0.9766 \\ EDSR [14] & 1370 K & 43.61/0.9853 & 33.50/0.9359 & 38.18/0.9797 \\ SRMD [41] & 1511 K & 42.26/0.9841 & 33.15/0.9335 & 37.27/0.9780 \\ SRMDNF [41] & 1511 K & 42.76/0.9848 & 33.30/0.9348 & 37.69/0.9794 \\ SRFBN-S [21] & 282 K & 42.77/0.9843 & 33.29/0.9342 & 38.88/0.9806 \\ FAWDN [22] & 7170 K & 43.35/0.9850 & 33.41/0.9352 & 37.91/0.9791 \\ FAWDN+ [22] & 7170 K & 43.59/0.9851 & 33.87/0.9400 & 38.10/0.9798 \\ DMFN(ours) & 475 K & 43.38/0.9850 & 33.41/0.9353 & 39.27/0.9813 \\ DMFN+(ours) & 475 K & 43.57/0.9851 & 33.84/0.9397 & 39.43/0.9818 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of PSNR/SSIM for differentscale factors on the MRI13 [22], ADNI100 [36], and OASIS100 [37] datasets. The red and blue represent the best and second-best results, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Attention Module**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Params**} & **MRI13** & **ADNI100** & **OASIS100** \\ \cline{3-5}  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline DMFN & & 583 k & 36.72/0.9485 & 30.17/0.8719 & 34.35/0.9478 \\ DMFN-CBAM & \multirow{2}{*}{x3} & 583 k & 36.40/0.9476 & 30.13/0.8721 & 34.21/0.9472 \\ DMFN-RCAB & & 582 k & 36.47/0.9478 & 30.13/0.8718 & 34.24/0.9473 \\ DMFN-CCA & & 582 k & 36.56/0.9479 & 30.14/0.8717 & 34.29/0.9475 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of existing attention models and our CRB on DMFN. _Appl. Sci._**2022**, _12_, 12794