## 3 Experimental Results and Discussion
### Results and Analyses
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Method** & **Dataset** & **SAM** & **ERGAS** & **UIQI** & **Time (s)** & **Dataset** & **SAM** & **ERGAS** & **UIQI** & **Time (s)** \\ \hline CNMF & & 4.12 & 5.70 & 0.9807 & 39.3 & & 10.11 & 7.51 & 0.9860 & 63.9 \\ JNMF & & 4.59 & 5.65 & 0.9774 & **14.2** & & 9.10 & 7.49 & 0.9858 & **21.3** \\ DPLM & University & 4.00 & 5.31 & 0.9826 & 49.9 & Pavia City & 10.72 & 8.64 & 0.9815 & 153.4 \\ TCNNF & of Pavia & 2.76 & 3.90 & 0.9911 & 3079.0/26.5 & center & 5.80 & 5.47 & 0.9925 & 4797.9/40.8 \\ DiCNN & & 3.06 & 4.25 & 0.9902 & 10,553.1/105.8 & 5.60 & 5.67 & 0.9917 & 16,545.7/169.2 \\ CpCNN & & **2.62** & **3.47** & **0.9927** & 1449.3/**8.5** & & **5.32** & **4.69** & **0.9943** & 2273.7/**13.1** \\ \hline CNMF & & 2.59 & 4.74 & 0.9777 & 43.4 & & 8.89 & 16.31 & 0.8157 & 15.6 \\ JNMF & & 3.21 & 6.16 & 0.9724 & **15.2** & & 8.96 & 17.71 & 0.8155 & 5.2 \\ \hline DPLM & Washington & 2.83 & 7.74 & 0.9703 & 56.9 & San & 4.33 & 10.78 & 0.9305 & 23.7 \\ TCNNF & DC Mall & 1.44 & 4.94 & 0.9904 & 3953.1/35.3 & Francisco & 2.87 & 6.53 & 0.9747 & 1434.4/13.5 \\ \hline DiCNN & & 1.33 & 2.90 & **0.9958** & 1357.3/114.9 & 2.45 & 5.56 & 0.9806 & 5140.8/53.2 \\ CpCNN & & **1.14** & **2.81** & **0.9958** & 1596.3/**11.5** & & **2.23** & **5.52** & **0.9819** & 603.7/**4.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical evaluation of hyper-sharpening results under resolution ratio of 2 (\(s=2\)). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Dataset** & **SAM** & **ERGAS** & **UIQI** & **Time (s)** & **Dataset** & **SAM** & **ERGAS** & **UIQI** & **Time (s)** \\ \hline CNMF & & 4.31 & 3.55 & 0.9709 & 38.3 & & 9.89 & 4.49 & 0.9797 & 61.1 \\ JNMF & & 4.73 & 2.93 & 0.9756 & **16.6** & & 9.13 & 3.82 & 0.9848 & **26.7** \\ \hline DPLM & University & 4.23 & 2.94 & 0.9782 & 46.0 & Pavia City & 11.81 & 4.75 & 0.9780 & 164.8 \\ TCNNF & of Pavia & 3.75 & 2.73 & 0.9823 & 711.5/24.3 & center & **6.75** & 3.66 & 0.9865 & 1103.5/38.9 \\ DiCNN & & 3.41 & 2.75 & 0.9843 & 2572.0/107.6 & 8.22 & 3.34 & 0.9891 & 4237.1/170.6 \\ \hline CpCNN & & **3.34** & **2.26** & **0.9889** & 382.2/**8.7** & **6.81** & **2.92** & **0.9915** & 5933.3/**13.3** \\ \hline CNMF & & 2.80 & 2.66 & 0.9708 & 41.0 & & 8.88 & 8.19 & 0.8046 & 15.9 \\ \hline JNMF & & 3.57 & 3.30 & 0.9681 & **17.2** & & 8.90 & 9.00 & 0.8030 & **6.5** \\ \hline DPLM & Washington & 3.27 & 4.04 & 0.9666 & 52.8 & & 4.81 & 5.74 & 0.9184 & 30.2 \\ \hline TCNNF & DC Mall & 2.14 & 2.93 & 0.9862 & 917.1/33.8 & & **4.29** & 5.28 & 0.9245 & 341.6/12.7 \\ \hline DiCNN & 1.72 & 2.00 & 0.9922 & 3399.6/145.6 & & 4.17 & 5.48 & 0.9415 & 1220.2/49.8 \\ \hline CpCNN & & **1.62** & **1.97** & **0.9931** & 444.6/**11.3** & & **3.84** & **4.56** & **0.9485** & 161.2/**4.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numerical evaluation of hyper-sharpening results under resolution ratio of 4 (\(s=4\)). in [38]. Some minor differences can be observed on the red roof in the lower right corner, where CNMF has slight color distortion compared with the others. The more apparent discrepancies can be observed on the lake of Figure 3, where we can see that the last three images have similar colors with the reference image, corresponding to the higher UIQIs (0.9865, 0.9891, and 0.9915, respectively) and lower SAMs (6.75, 8.22, and 6.81) as well as the ERGASs (3.66, 3.34, and 2.92) values than the conventional methods. Likewise, in Figures 4 and 5, CNN-based images usually present approximate spectral characteristics. Similarly, the SAMs achieve 2.14, 1.72, and 1.62, and the UIQIs are higher than 0.98, for the Washington DC Mall dataset, which performs much better than the former ones. Since the last dataset contains HS and MS images acquired by different platforms, the fused images exhibit large color distortions.