# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Results on Remote-Sensing Datasets
We also present the visual results of several SR methods for \(\times\)4 SR on UC-Merced in Figure 6. It is clear that our method demonstrates better recovery effects on object contours and detailed textures compared with other methods. Specifically, in the scenes with strong global consistency like 'agricultural21', only DAFEN adequately recovers texture information. Similarly, in scenes with scale variations, such as 'airplane67', DAFEN achieves more precise edge details. Notably, for the parking lot in'mobilehomerpark92' and the tennis court boundaries in 'tenniscourt93', other methods exhibit varying degrees of distortion. For example, in'mobilehomerpark92', other methods reconstruct blurred parking lot lines, while our DAFEN reconstructs more accurate parking lot lines. Additionally, in 'tenniscourt93', the reconstruction results of methods like IDN exhibit blurred tennis court boundaries, while methods such as AMFFN and CTN exhibit boundary distortion. In contrast, our DAFEN achieves results that are closer to the ground truth. However, the reconstruction results of our DAFEN-S exhibit some incorrect information, such as blurred parking lot lines in'mobilehomerpark92'. This is because DAFEN-S has fewer parameters compared with DAFEN, resulting in weaker anti-interference ability. Overall, the proposed DAFEN demonstrates superior visual performance compared with other methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Scale** & **Params** & **Multi-Adds** & \
\begin{tabular}{c} **RS-T1** \\ **PSNR/SSIM** \\ \end{tabular} &
\begin{tabular}{c} **RS-T2** \\ **PSNR/SSIM** \\ \end{tabular} \\ \hline Bicubic & \(\times\)3 & - & - & 29.73/0.7818 & 27.23/0.7697 \\ LGCNet [21] & \(\times\)3 & 193 K & 79.0 G & 31.30/0.8314 & 29.03/0.8312 \\ IDN [51] & \(\times\)3 & 553 K & 56.3 G & 31.73/0.8430 & 29.59/0.8450 \\ LESRCNN [73] & \(\times\)3 & 810 K & 238.9 G & 31.68/0.8398 & 29.65/0.8444 \\ CTN [48] & \(\times\)3 & 402 K & 37.1 G & 31.91/0.8454 & 29.83/0.8489 \\ FeNet-baseline [46] & \(\times\)3 & 163 K & 16.7 G & 31.73/0.8377 & 29.61/0.8446 \\ FeNet [46] & \(\times\)3 & 357 K & 35.2 G & 31.89/0.8432 & 29.80/0.8481 \\ FDENet [57] & \(\times\)3 & 488 K & 61.7 G & 31.98/0.8488 & 29.88/0.8489 \\ DARN-S [56] & \(\times\)3 & 355 K & 35.0 G & 32.00/0.8483 & 29.98/0.8518 \\ AMFFN [41] & \(\times\)3 & 305 K & 27.9 G & 31.94/0.8457 & 29.91/0.8504 \\ IFIN-S [55] & \(\times\)3 & 459 K & 51.0 G & 32.04/0.8448 & 30.03/0.8535 \\ BMFENet [58] & \(\times\)3 & 470 K & 51.7 G & 31.99/0.8465 & 29.97/0.8514 \\ DAFEN-S (ours) & \(\times\)3 & 192 K & 17.1 G & 31.93/0.8459 & 29.81/0.8485 \\ DAFEN (ours) & \(\times\)3 & 422 K & 37.7 G & 32.13/0.8527 & 29.98/0.8516 \\ \hline Bicubic & \(\times\)4 & - & - & 27.91/0.6968 & 25.40/0.6770 \\ LGCNet [21] & \(\times\)4 & 193 K & 44.5 G & 29.13/0.7481 & 26.76/0.7426 \\ IDN [51] & \(\times\)4 & 553 K & 32.3 G & 29.56/0.7623 & 27.31/0.7627 \\ LESRCNN [73] & \(\times\)4 & 774 K & 241.6 G & 29.62/0.7625 & 27.41/0.7646 \\ CTN [48] & \(\times\)4 & 413 K & 25.6 G & 29.71/0.7666 & 27.52/0.7704 \\ FeNet-baseline [46] & \(\times\)4 & 169 K & 9.4 G & 29.57/0.7626 & 27.31/0.7619 \\ FeNet [46] & \(\times\)4 & 366 K & 20.4 G & 29.70/0.7688 & 27.45/0.7672 \\ FDENet [57] & \(\times\)4 & 501 K & 35.9 G & 29.72/0.7658 & 27.54/0.7697 \\ DARN-S [56] & \(\times\)4 & 363 K & 19.7 G & 29.78/0.7682 & 27.59/0.7732 \\ AMFFN [41] & \(\times\)4 & 314 K & 16.2 G & 29.76/0.7674 & 27.57/0.7701 \\ IFIN-S [55] & \(\times\)4 & 470 K & 31.6 G & 29.84/0.7724 & 27.68/0.7763 \\ BMFENet [58] & \(\times\)4 & 477 K & 29.4 G & 29.81/0.7700 & 27.62/0.7730 \\ DAFEN-S (ours) & \(\times\)4 & 198 K & 10.0 G & 29.70/0.7673 & 27.46/0.7677 \\ DAFEN (ours) & \(\times\)4 & 431K & 21.8 G & 29.82/0.7701 & 27.62/0.7737 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._