## 4 Experimental Results
### Evaluation
#### 4.2.1 Evaluate under Noise-Free Conditions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**2\(\mathbf{x}\)**} & \multicolumn{4}{c}{**4\(\mathbf{x}\)**} & \multicolumn{4}{c}{**8\(\mathbf{x}\)**} \\ \cline{2-13}  & **Cones** & **Teddy** & **Tsukuba** & **Venus** & **Cones** & **Teddy** & **Tsukuba** & **Venus** & **Cones** & **Teddy** & **Tsukuba** & **Venus** \\ \hline Bicubic & 2.51 & 1.93 & 5.82 & 1.31 & 3.52 & 2.86 & 8.67 & 1.91 & 5.36 & 4.04 & 12.31 & 2.76 \\ JID [34] & 1.58 & 1.27 & 3.83 & 0.75 & 3.23 & 1.98 & 6.13 & 1.02 & 4.92 & 2.94 & 9.57 & 1.37 \\ SRCNN [9] & 1.76 & 1.35 & 3.82 & 0.84 & 2.98 & 1.94 & 5.91 & 1.11 & 4.94 & 2.92 & 9.15 & 1.45 \\ LapSRN [35] & 0.98 & 0.97 & 1.72 & 0.72 & 2.85 & 1.68 & 5.34 & 0.77 & 4.67 & 2.96 & 8.94 & 1.34 \\ MSG-Net [23] & 1.13 & 0.99 & 2.20 & 0.59 & 2.95 & 1.78 & 5.21 & 0.78 & 5.23 & 3.18 & 10.25 & 1.18 \\ MFR-SR [24] & 1.27 & 1.08 & 2.65 & 0.54 & 2.43 & 1.75 & 5.63 & 0.85 & 4.73 & 2.49 & 8.03 & 1.25 \\ RDN-GDE [38] & 0.88 & 0.85 & 1.41 & 0.56 & 2.38 & 1.58 & 3.73 & 0.73 & 4.66 & 2.88 & 7.79 & 1.09 \\ DSDMSR [13] & 1.04 & 0.83 & 1.80 & 0.53 & 2.19 & 1.54 & 4.15 & 0.78 & 4.70 & 3.06 & 7.43 & 1.28 \\ ours & **0.69** & **0.74** & **1.13** & **0.39** & **1.72** & **1.26** & **2.94** & **0.53** & **4.28** & **2.12** & **7.11** & **0.76** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative Analysis of Noise-Free DSR Results (RMSE) on Dataset A. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Art**} & \multicolumn{4}{c}{**Books**} & \multicolumn{4}{c}{**[PERSON]**} \\ \cline{2-13}  & **2\(\mathbf{x}\)** & **4\(\mathbf{x}\)** & **8\(\mathbf{x}\)** & **16\(\mathbf{x}\)** & **2\(\mathbf{x}\)** & **4\(\mathbf{x}\)** & **8\(\mathbf{x}\)** & **16\(\mathbf{x}\)** & **2\(\mathbf{x}\)** & **4\(\mathbf{x}\)** & **8\(\mathbf{x}\)** & **16\(\mathbf{x}\)** \\ \hline Bicubic & 2.57 & 3.85 & 5.53 & 8.37 & 1.01 & 1.58 & 2.27 & 3.36 & 0.91 & 1.35 & 2.06 & 2.97 \\ JID [34] & 1.16 & 1.84 & 2.77 & 10.86 & 0.59 & 0.93 & 1.14 & 9.15 & 0.61 & 0.87 & 1.37 & 10.3 \\ SRCNN [9] & 0.98 & 2.09 & 4.75 & 7.81 & 0.39 & 0.94 & 2.15 & 3.24 & 0.45 & 0.97 & 2.01 & 2.82 \\ LapSRN [35] & 0.88 & 1.79 & 2.73 & 6.31 & 0.78 & 0.94 & 1.29 & 2.35 & 0.77 & 0.95 & 1.33 & 2.37 \\ AR [36] & 3.07 & 3.99 & 4.68 & 6.87 & 1.38 & 1.94 & 2.05 & 2.84 & 0.98 & 1.23 & 1.73 & 2.56 \\ VDSR [37] & 1.32 & 2.07 & 3.24 they may introduce incorrect depth information in certain regions, leading to the issue of depth artifacts. In comparison, our method excels in recovering clear details and effectively addresses the issue of edge artifacts. This is primarily attributed to the multi-branch feature fusion module we proposed, which can fully explore the consistency between the HR color image and the LR depth image. By making reasonable predictions for less obvious details, our method achieves superior results in depth map reconstruction. Figure 4: The visual comparison of the x8 upsampled depth images without noise on the Middlebury datasets: (**a**) Ground Truth; (**b**) Biucbic; (**c**) LapSRN [35]; (**d**) SRCNN [9]; (**e**) MSG-Net [23]; (**f**) MFR-SR [24]; (**g**) proposed method.