## 5 Comparisons with SOTA Methods
### Experiments on Reduced-Resolution Datasets  
For the QB dataset, Table 6 presents the quantitative evaluation performance results. These results indicate that DCMFN achieves the highest ranking across all metrics, closely followed by SSIN. This is despite the fact that our method ranks second in the Structural Similarity Measure (SAM). It is notable that the difference between our results and the best performance by SSIN is minimal, differing only by a fraction of a decimal point. For the remaining two datasets, WV2 and WV4, the quantitative evaluation performance results are presented in Tables 7 and 8, where DCMFN consistently outperforms all indicators on the WV4 dataset, particularly excelling in the SAM and ERGAS metrics. In addition to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modules** & \ \begin{tabular}{c} **Without,** \\ **C1+C2** \\ \end{tabular}  & **C1** & **C2** & \begin{tabular}{c} **Parallel,** \\ **C1+C2** \\ \end{tabular}  \\ \hline
**SAM\(\downarrow\)** & 1.6050 & 1.5244 & 1.4701 & **1.4240** \\
**ERGAS\(\downarrow\)** & 1.5103 & 1.3793 & 1.3045 & **1.2587** \\
**Q2\({}^{\mathbf{n}}\uparrow\)** & 0.8751 & 0.8845 & 0.8892 & **0.8924** \\
**CC\(\uparrow\)** & 0.9736 & 0.9770 & 0.9788 & **0.9901** \\
**D\({}_{\mathbf{X}}  
the quantitative assessments, this experiment also compares the subjective visual results. It offers a comprehensive evaluation of the effectiveness of different methods across the three different datasets mentioned above. In order to highlight the abilities of DCMFN in maintaining spectral integrity and enhancing detail, images with rich detail and spectral characteristics are specifically selected for visual examination. The visual results from different methods are showcased in Figures 10-12, featuring random selections from the three datasets. The first two rows exhibit RGB images. Conversely, the next two rows depict images that result from averaging the residuals across different bands. Figure 10 illustrates, for the QB dataset, traditional methods, such as EXP, BDSD-PC, and PRACS. Fuzzy results are produced when the residuals of each band are averaged, particularly with EXP and PRACS, which inject little to no detail. Although these traditional methods generate pansharpened images, they lack sufficient detail in contrast to the ground truth (GT). In contrast, the eight deep-learning-based methods produce vivid and clear results, effectively capturing intricate details. The analysis of the residual images indicates that the performance of DCMFN closely aligns with that of GT, demonstrating that DCMFN exhibits superior capabilities in both detail recovery and spectral fidelity. Figure 11 presents similar findings. The results achieved by all methods on the WV2 dataset are marginally lower in quality compared to those observed with the QB dataset, which we hypothesize is due to the WV2 dataset containing twice as many bands as the QB dataset. Furthermore, aside from DCMFN, other deep learning-based methods exhibit different levels of spectral distortion, which is shown by the white roof located in the lower left corner of the image. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & **SAM \(\downarrow\)** & **ERGAS \(\downarrow\)** & **Q2N \(\uparrow\)** & **CC \(\uparrow\)** \\ \hline
**EXP** & 1.8769 & 1.8077 & 0.6922 & 0.8351 \\
**BDSD-PC** & 0.8769 & 0.7548 & 0.9483 & 0.9662 \\
**GSA** & 0.8582 & 0.7634 & 0.9461 & 0.9664 \\
**PRACS** & 0.9005 & 0.8011 & 0.9394 & 0.9657 \\
**MTF-GLP** & 0.8307 & 0.7308 & 0.9498 & 0.9686 \\
**PNN** & 0.9998 & 0.9341 & 0.8663 & 0.9610 \\
**PanNet** & 0.9990 & 0.7754 & 0.8874 & 0.9703 \\
**MSDCNN** & 0.9978 & 0.7711 & 0.8833 & 0.9696 \\
**TFNET** & 0.7951 & 0.6037 & 0.9090 & 0.9810 \\
**GGPCRN** & 0.7993 & 0.6021 & 0.8925 & 0.9830 \\
**MUCNN** & 0.9239 & 0.7207 & 0.8935 & 0.9732 \\
**PanFormer** & 0.8483 & 0.6441 & 0.8996 & 0.9776 \\
**SSIN** & 0.7136 & 0.6020 & 0.9067 & 0.9845 \\
**DCMFN (ours)** & **0.7033** & **0.6011** & **0.9294** & **0.9992** \\ \hline \hline \end{tabular}
\end{table}