# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Results on Remote-Sensing Datasets  
To validate the effectiveness of the proposed method on remote-sensing datasets, DAFEN and DAFEN-S are compared with existing lightweight models, including LGCNet [21], IDN [51], LESRCNN [73], CTN [48], FeNet [46], FDENet [57], DARN-S [56], AMFFN [41], IFIN-S [55], and BMFENet [58]. These models have been published in high-quality journals or conferences. All models compared in the paper are tested directly on remote-sensing data using pre-trained models provided by the respective researchers. Additionally, the training set for all comparison models is DIV2K, ensuring fairness in the comparison results. Table 3 shows the quantitative comparison results of different methods under all scaling factors. It can be seen that DAFEN outperforms existing methods or achieves comparable performance. Based on the scaling factor of \(\times\)4, IFIN-S, with approximately 470 K parameters, provides better results on the two RSI datasets. In contrast, DAFEN achieves a similar performance level with around 431K parameters and lower computational requirements. Additionally, DAFEN's performance is comparable to BMFENet, which has a larger number of parameters and computational load. Moreover, our DAFEN-S has a similar number of parameters to FeNet-baseline and LGCNet, but DAFEN-S performs much better. Furthermore, our DAFEN-S outperforms FeNet with 351 K parameters in most test experiments, with only 188 K parameters. This is mainly due to the clever design of DAFEN, which effectively enriches, fuses, and enhances features while reducing model redundancy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variant** & **Params** & **Multi-Adds** & **RS-T1(x4)** \\  & & & **PSNR/SSIM** \\ \hline Loss type: \(L_{2}\) & 431 K & 21.8 G & 29.73/0.7669 \\ batch size: 32 & 431 K & 21.8 G & 29.81/0.7694 \\ batch size: 8 & 431 K & 21.8 G & 29.80/0.7689 \\ patch size: 256 & 431 K & 38.8 G & 29.82/0.7695 \\ patch size: 128 & 431 K & 9.7 G & 29.80/0.7691 \\ Use\_hflip: false & 431 K & 21.8 G & 29.76/0.7681 \\ Use\_rot: false & 431 K & 21.8 G & 29.78/0.7685 \\ DAFEN & 431 K & 21.8 G & 29.82/0.7701 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Impact of hyperparameter selection on model performance and complexity at a scaling factor of \(\times\)4 on the RS-T1 dataset. The Multi-Adds is calculated corresponding to a \(1280\times 720\) HR image. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Scale** & **Params** & **Multi-Adds** & **RS-T1** & **RS-T2** \\  & & & & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Bicubic & \(\times\)2 & - & - & 33.25/0.8934 & 30.64/0.8837 \\ LGCNet [21] & \(\times\)2 & 193 K & 178.1 G & 35.65/0.9298 & 33.47/0.9281 \\ IDN [51] & \(\times\)2 & 553 K & 124.6 G & 36.13/0.9339 & 34.07/0.9329 \\ LESRCNN [73] & \(\times\)2 & 626 K & 281.5 G & 36.04/0.9328 & 34.00/0.9320 \\ CTN [48] & \(\times\)2 & 402 K & 60.9 G & 36.30/0.9243 & 34.31/0.9346 \\ FeNet-baseline [46] & \(\times\)2 & 158 K & 35.2 G & 36.10/0.9331 & 34.10/0.9326 \\ FeNet [46] & \(\times\)2 & 351 K & 77.9 G & 36.23/0.9341 & 34.22/0.9337 \\ FDENet [57] & \(\times\)2 & 480 K & 138.7 G & 36.26/0.9346 & 34.28/0.9338 \\ DARN-S [56] & \(\times\)2 & 350 K & 78.9 G & 36.31/0.9347 & 34.35/0.9348 \\ AMFFN [41] & \(\times\)2 & 298 K & 61.5 G & 36.39/0.9357 & 34.43/0.9346 \\ IFIN-S [55] & \(\times\)2 & 451 K & 110.6 G & 36.38/0.9356 & 34.42/0.9352 \\ BMFENet [58] & \(\times\)2 & 465 K & 115.0 G & 36.42/0.9362 & 34.43/0.9356 \\ DAFEN-S (ours) & \(\times\)2 & 188 K & 37.5 G & 36.28/0.9352 & 34.24/0.9338 \\ DAFEN (ours) & \(\times\)2 & 416 K & 83.6 G & 36.42/0.9365 & 34.39/0.9357 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation results for SR on two RSI test datasets. PSNR and SSIM values are provided. \(\cdot\)\(\cdot\)\(\cdot\) denotes the results are not provided. The best and second-best results are highlighted in red and blue, respectively. The Multi-Adds is calculated corresponding to a \(1280\times 720\) HR image.