## 5 Comparisons with SOTA Methods
### Experiments on Full-Resolution Datasets  
To assess the generalization capabilities of the discussed methods, extensive experiments were conducted at full resolution. Quantitative results for the above three datasets are presented in Tables 9-11, respectively. It is essential to highlight that the EXP method, which upscales LRMS images without adding detail, is excluded from this comparison. The results across the three datasets indicate that except \(D_{S}\) on the QB dataset is the second best, our proposed DCMFN method ultimately surpasses all other methods across all evaluation metrics, with SSIN following closely behind. Notably, the \(D_{\lambda}\) and \(D_{S}\) metrics for the QB dataset are significantly higher than other deep learning-based methods. Similarly, on WV2 and WV4 datasets, \(D_{\lambda}\) also shows substantial improvements over the other methods. Figures 13-15 show the FR visual results of all methods for three datasets, featuring random selections from the three datasets. Quantitative results for the three datasets are provided separately. It is obvious that the EXP method, which upscales low-resolution multispectral (LRMS) images without adding detail, is excluded from this comparison. The results across the three datasets indicate that except \(D_{S}\) on the QB dataset is the second best, DCMFN method ultimately surpasses all other methods across all evaluation metrics, with SSIN following closely behind. Notably, the \(D_{\lambda}\) and \(D_{S}\) metrics for the QB dataset are significantly higher than other deep learning-based methods. Similarly, on the WV2 and WV4 datasets, \(D_{\lambda}\) also shows substantial improvements over the other methods. Furthermore, the full-resolution visualization results of various methods are conducted across the three datasets as illustrated in Figures 13-15. In Figure 13, it is evident that deep learning-based methods, including PNN, PANNET, and particularly TFNET and GGPCRN, exhibit significant spectral distortion in the magnified regions of the image. The traditional pansharpening method yields average visual results, often resulting in blurry output. In contrast, DCMFN delivers the best visual quality, showcasing a more vivid image, particularly excelling in edge recovery, such as that of the stones, with higher clarity and reduced spectral distortion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(D_{\lambda}^{K}\downarrow\) & \(\mathbf{D_{S}}\downarrow\) & \(\mathbf{HQNR}\uparrow\) \\ \hline
**EXP** & 0.0510 & 0.1016 & 0.7648 \\
**GSA** & 0.1269 & 0.1743 & 0.7287 \\
**PRACS** & 0.0836 & 0.1096 & 0.8197 \\
**MTF-GLP** & 0.0694 & 0.2004 & 0.7483 \\
**PNN** & 0.0999 & 0.1201 & 0.1975 \\
**PanNet** & 0.0635 & 0.9070 & 0.8542 \\
**MSDCNN** & 0.0946 & 0.1012 & 0.8192 \\
**TFNET** & 0.0474 & 0.0591 & 0.8975 \\
**GGPCRN** & 0.0528 & 0.0608 & 0.8911 \\
**MUCNN** & 0.0946 & 0.1012 & 0.8192 \\
**PanFormer** & 0.0802 & 0.0610 & 0.8655 \\
**SSIN** & 0.0477 & 0.0646 & 0.8918 \\
**DCMFN (ours)** & **0.0242** & **0.0357** & **0.9410** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Quantitative results of different methods at full resolution for the QB dataset. In Figure 14, the enlarged area reveals that the results from traditional methods exhibit poor panchromatic sharpening effects, resulting in images that lack vibrancy and display significant spectral distortion. Conversely, most deep learning-based methods yield improved results; however, the local magnified section highlights a specific spectral distortion in all methods except for DCMFN, particularly noticeable in the middle rectangular light