# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Ablation Study
#### 4.8.3 Effects of the FFEM  
The forward fusion structure, SCGC, and CCA combine to form a powerful FFEM. To demonstrate the effectiveness of these key modules, we compare FFEM with its variants, as shown in Table 9. For the forward fusion structure, we compare it with the 1 \(\times\) 1 convolution fusion method and the BFM [16], as seen in the first, second, and fifth rows \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variant** & **Params** & **Multi-Adds** & \begin{tabular}{c} **RS-T1** \\ **PSNR/SSIM** \\ \end{tabular} & \begin{tabular}{c} **RS-T2** \\ **PSNR/SSIM** \\ \end{tabular} & \begin{tabular}{c} **BSD100** \\ **PSNR/SSIM** \\ \end{tabular} & \begin{tabular}{c} **Urban100** \\ **PSNR/SSIM** \\ \end{tabular} \\ \hline \begin{tabular}{c} DAFEN w/o \\ DAFEB \\ DAFEN w/o \\ CSLB \\ DAFEN w/o \\ DAFEN w/o \\ FFEM \\ DAFEN \\ DAFEN \\ DAFEN \\ DAFEN \\ \end{tabular} & 683 K & 38.7 G & 29.82/0.7689 & 27.63/0.7744 & 27.58/0.7380 & 26.03/0.7840 \\ \hline \
\begin{tabular}{c} DAFEN w/o \\ CFEM \\ DAFEN \\ DAFEN \\ DAFEN \\ DAFEN \\ DAFEN \\ \end{tabular} & 744 K & 40.2 G & 29.83/0.7694 & 27.61/0.7742 & 27.58/0.7383 & 26.05/0.7851 \\ \hline
\begin{tabular}{c} DAFEN w/o \\ CFEM \\ DAFEN \\ DAFEN \\ DAFEN \\ \end{tabular} & 315 K & 16.5 G & 29.75/0.7663 & 27.54/0.7706 & 27.52/0.7353 & 25.92/0.7794 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation experiments on the design of the 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
**Variant** & **Params** & **Multi-Adds** & \begin{tabular}{c} **RS-T1** \\ **PSNR/SSIM** \\ \end{tabular} & \begin{tabular}{c} **RS-T2** \\ **PSNR/SSIM** \\ \end{tabular} & \begin{tabular}{c} **BSD100** \\ **PSNR/SSIM** \\ \end{tabular} & \begin{tabular}{c} **Urban100** \\ **PSNR/SSIM** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} CSLB w/o \\ GRSB \\ CSLB w/o \\ CSAIM \\ CSLB \\ \end{tabular} & 867 K & 46.9 G & 29.85/0.7708 & 27.68/0.7753 & 27.61/0.7386 & 26.13/0.7854 \\ \hline
\begin{tabular}{c} CSLB w/o \\ CSAIM \\ CSLB \\ \end{tabular} & 434 K & 21.7 G & 29.80/0.7696 & 27.61/0.7735 & 27.54/0.7371 & 25.99/0.7831 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation experiments on the design of the CSLB on RS-T1, RS-T2, BSD100, and Urban100 datasets for \(\times\)4 SR.  
of the table. It is clear that our forward fusion structure has fewer parameters than the 1 \(\times\) 1 convolution method and leads in PSNR and SSIM values across all four test sets. Furthermore, compared with BFM, our forward fusion structure improves the PSNR by 0.03 dB, 0.01 dB, and 0.01 dB on RS-T1, RS-T2, and BSD100, respectively, and achieves better SSIM values on RS-T1, RS-T2, BSD100, and Urban100. To further demonstrate the effectiveness of the forward fusion structure, we compare the visualization results of 1 \(\times\) 1 convolution fusion, BFM, and our FFEM using average feature maps, as shown in Figure 10. It can be observed that our FFEM achieves better edge and texture details, both in the local DAFEB and the global DAFEN. This fully proves the effectiveness of the forward fusion structure and supports the rationale of allocating more channels to higher-level features in multi-level feature fusion to retain more high-level feature information. To demonstrate the effectiveness of the dual-branch structure, we conduct two experiments using CCA and SCGC individually, as shown in the third and fourth rows of Table 9. It can be seen that the combined version of CCA and SCGC achieves better performance compared with using a single module. Although using CCA or SCGC alone achieves lower parameter counts and computational costs, this comes at the expense of significantly reduced accuracy. To gain deeper insights into the performance enhancement achieved by FFEM, we employ a Local Attribution Map (LAM) [75]. As shown in Figure 11, the LAM results clearly demonstrate that the combined version of CCA and SCGC significantly enhances attention to important information.