# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Ablation Study
#### 4.8.6 Influence of the Number of DAFEBs and CSLBs  
To further optimize network parameters and performance, we investigate the impact of different numbers of DAFEB and CSLB on the model, as shown in Table 12. Specifically, \(n_{b}\) represents the number of DAFEB, while \(n_{l}\) denotes the number of CSLB. Initially, when the number of CSLBs is 1, the network's performance is the worst. As the number of CSLBs increases, such as 1, 3, and 5, the model's performance continuously improves, indicating that our DAFEB structure has the potential to achieve top-level performance when used in larger networks. Additionally, we compare different numbers of DAFEB, such as 3, 4, 5, and 6. With the increase in the number of DAFEB, SR performance, parameter count, and computational cost also increase accordingly. Considering our goal is to study lightweight RSISR, to achieve a more reasonable balance between performance and lightweight design, we set the number of DAFEB to 4 and the number of CSLB to 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**Multi-Adds**} & **RS-T1** & **RS-T2** & **BSD100** & **Urban100** \\  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline W/ PSConv & 368 K & 18.7 G & 29.73/0.7665 & 27.54/0.7706 & 27.48/0.7346 & 25.89/0.7792 \\ W/ BSConv & 343 K & 17.5 G & 29.62/0.7634 & 27.42/0.7663 & 27.44/0.7317 & 25.80/0.7768 \\ W/ DSConv & 343 K & 17.5 G & 29.65/0.7642 & 27.40/0.7648 & 27.45/0.7324 & 25.81/0.7766 \\ DAFEN & 431 K & 21.8 G & 29.82/0.7701 & 27.62/0.7737 & 27.57/0.7376 & 26.01/0.7832 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation experiments on the design of the lightweight convolution in DAFEN on RS-T1, RS-T2, BSD100, and Urban100 datasets for \(\times\)4 SR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**Multi-Adds**} & **RS-T1** & **RS-T2** & **BSD100** & **Urban100** \\  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline G = 2 & 628 K & 31.8 G & 29.84/0.7705 & 27.66/0.7746 & 27.59/0.7385 & 26.08/0.7844 \\ G = 4 & 431 K & 21.8 G & 29.82/0.7701 & 27.62/0.7737 & 27.57/0.7376 & 26.01/0.7832 \\ G = 8 & 333 K & 16.8 G & 29.77/0.7672 & 27.59/0.7727 & 27.54/0.7365 & 25.94/0.7808 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Ablation experiments on the design of the number of groups in group convolution on RS-T1, RS-T2, BSD100, and Urban100 datasets for \(\times\)4 SR.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**Multi-Adds**} & **RS-T1** & **RS-T2** & **BSD100** & **Urban100** \\  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline \(n_{b}=4\), \(n_{l}=1\) & 289 K & 13.9 G & 29.76/0.7681 & 27.57/0.7716 & 27.53/0.7361 & 25.86/0.7787 \\ \(n_{b}=4\), \(n_{l}=3\) & 431 K & 21.8 G & 29.82/0.7701 & 27.62/0.7737 & 27.57/0.7376 & 26.01/0.7832 \\ \(n_{b}=4\), \(n_{l}=5\) & 574 K & 29.7 G & 29.86/0.7694 & 27.71/0.7759 & 27.59/0.7383 & 26.11/0.7861 \\ \(n_{b}=3\), \(n_{l}=3\) & 345 K & 17.5 G & 29.79/0.7676 & 27.60/0.7730 & 27.54/0.7362 & 25.92/0.7798 \\ \(n_{b}=5\), \(n_{l}=3\) & 517 K & 26.0 G & 29.84/0.7700 & 27.65/0.7746 & 27.59/0.7380 & 26.05/0.7849 \\ \(n_{b}=6\), \(n_{l}=3\) & 603 K & 30.3 G & 29.86/0.7704 & 27.70/0.7764 & 27.61/0.7386 & 26.12/0.7863 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Ablation experiments on the design of the number of DAFEBs and CSLBs on RS-T1, RS-T2, BSD100, and Urban100 datasets for \(\times\)4 SR.