## 5 Comparisons with SOTA Methods
### Experiments on Full-Resolution Datasets
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(D_{\text{A}}^{\text{K}}\downarrow\) & \(\mathbf{D_{\text{S}}}\downarrow\) & **HQNR\(\uparrow\)** \\ \hline
**EXP** & 0.1970 & 0.1052 & 0.7089 \\
**BDSD-PC** & 0.2478 & 0.1428 & 0.6586 \\
**GSA** & 0.1988 & 0.1690 & 0.6811 \\
**PRACS** & 0.1294 & 0.0958 & 0.7944 \\
**MTF-GLP** & 0.0932 & 0.1762 & 0.7567 \\
**PNN** & 0.2788 & 0.0979 & 0.6565 \\
**PanNet** & 0.2072 & 0.1008 & 0.7180 \\
**MSDCNN** & 0.2215 & 0.1044 & 0.7042 \\
**TFNET** & 0.1918 & 0.1041 & 0.7294 \\
**GGPCRN** & 0.1800 & 0.0983 & 0.7455 \\
**MUCNN** & 0.2352 & 0.1188 & 0.6857 \\
**PanFormer** & 0.2771 & 0.0974 & 0.6611 \\
**SSIN** & 0.1696 & **0.0894** & 0.7573 \\
**DCMFN (ours)** & **0.0824** & 0.0952 & **0.8302** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Quantitative results of different methods at full resolution for the WV2 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(D_{\text{A}}^{\text{K}}\downarrow\) & \(\mathbf{D_{\text{S}}}\downarrow\) & **HQNR\(\uparrow\)** \\ \hline
**EXP** & 0.0800 & 0.0819 & 0.8479 \\
**BDSD-PC** & 0.1567 & 0.1258 & 0.7407 \\
**GSA** & 0.2097 & 0.1576 & 0.6700 \\
**PRACS** & 0.1233 & 0.0975 & 0.7930 \\
**MTF-GLP** & 0.0821 & 0.1332 & 0.7984 \\
**PNN** & 0.1090 & 0.0591 & 0.8496 \\
**PanNet** & 0.0911 & 0.0560 & 0.0859 \\
**MSDCNN** & 0.1007 & 0.0534 & 0.8544 \\
**TFNET** & 0.0965 & 0.0545 & 0.8558 \\
**GGPCRN** & 0.0974 & 0.0563 & 0.8527 \\
**MUCNN** & 0.0963 & 0.5470 & 0.8564 \\
**PanFormer** & 0.1121 & 0.0542 & 0.8512 \\
**SSIN** & 0.0890 & 0.0533 & 0.8634 \\
**DCMFN (ours)** & **0.0786** & **0.0529** & **0.8727** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Quantitative results of different methods at full resolution for the WV4 dataset. gray roof. Additionally, in the lower left corner, the dark gray roof shows edge distortion in many methods, whereas DCMFN demonstrates relatively minimal distortion in this region. In Figure 15, similar to Figure 14, the traditional method yields unsatisfactory results, while the deep learning-based methods produce more vibrant panchromatic sharpening outcomes. Among these, the white roof in the lower right corner of the magnified area exhibits significant spectral distortion, with DCMFN showing reduced distortion compared to other methods. Additionally, the edge of the yellow roof in the same area displays serious distortion in all methods except for DCMFN and MSDCNN. In conclusion, the experimental results from three urban remote sensing datasets, encompassing both RR and FR data, demonstrate that our DCMFN effectively balances spectral and spatial fidelity. In Table 12, we present the inference time and the scale of models based on deep learning. Specifically, we provide the average time cost for each image within the WorldView3 testing set. For models relying on Generative Adversarial Networks (GANs), we only take into account the parameters within the generator. As shown in Table 12, the number of parameters in our method is comparable to that of PanFormer and GGPCRN. However, our method consumes more time compared to CNN-based approaches. The reason lies in the fact that calculating attention requires more time than performing convolution operations. Figure 14: The subjective assessments at full resolution for the WV2 dataset. Figure 15: The subjective assessments at full resolution for the WV4 dataset. Figure 13: The subjective assessments at full resolution for the QB dataset.