# Video Super Resolution Using a Selective Edge Aggregation Network
## 4 Experiment
### Comparison with State-of-the-Art Methods  
We compared our model with a total of three models: RBPN, MuCAN, and EDVR. If you look at Tables 6 and 7, you can see the results of applying refinement (Stage 2) to the comparison model and our model. RBPN performance before using refinement in Vid4 is higher than our model performance, but lower in REDS4. This is because artifacts exist in Vid4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Combined Method** & **Stage 1** & **RBPN** & **MuCAN** & **EDVR** \\ \hline w/o Refinement & 27.02/ & 27.12/ & 27.26/ & 27.35/ \\  & 0.8136 & 0.8180 & 0.8215 & 0.8264 \\ \hline Bicubic Refinement & 27.54/ & 27.15/ & 27.47/ & 27.92/ \\  & 0.8182 & 0.8199 & 0.8215 & 0.8261 \\ \hline SRCNN Refinement & 27.79/ & 27.32/ & 27.48/ & 27.76/ \\  & 0.8439 & 0.8247 & 0.8312 & 0.8357 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative results (PSNR/SSIM) for the Vid4 test set when the refinement module is combined.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** \\ \hline Stagel + refinement module (BASE) & 27.52 & 0.8178 \\ Stagel + refinement module (VDLayer = 15) & 26.91 & 0.8119 \\ Stagel + refinement module (VDLayer = 19) & 27.53 & 0.8180 \\ Stagel + refinement module (VDLayer = 23) & 26.76 & 0.8082 \\ Stagel + refinement module (MVDLayers = 17) & 27.54 & 0.8182 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation of various refinement module structures. All of the sigma values for Canny edge detection are 0.7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Combined Method** & **Stage 1** & **RBPN** & **MuCAN** & **EDVR** \\ \hline w/o Refinement & 30.40/ & 30.09/ & 30.88/ & 31.09/ \\  & 0.8670 & 0.8590 & 0.8750 & 0.8800 \\ \hline Bicubic Refinement & 30.71/ & 30.12/ & 31.02/ & 31.01/ \\  & 0.8613 & 0.8633 & 0.8771 & 0.8804 \\ \hline SRCNN Refinement & 32.32/ & 30.34/ & 31.69/ & 31.01/ \\  & 0.9069 & 0.8642 & 0.8951 & 0.8804 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative results (PSNR/SSIM) for the REDS4 test set when the refinement module is combined.  
Similar to our model, an edge map was used to edge-aware loss in MuCAN, where the performance growth rate after using the refinement in Vid4 is lower than that in REDS4. The performance is also lower than EDVR performance in Vid4, but higher in REDS4.  
Additionally, if you look at Figure 7, you can see the results before and after applying the Refinement module. Our model's and MuCAN's output became clearer than the output without the refinement applied, but in the case of RBPN, a blur area was created. From this point of view, it can be said that the edge map created in Vid4 was applied as noise when training Stage 1 of our model, and the edge map created in REDS4 acted as effective information.  
Similar to the training of Stage 1 of our model, the edge map of each frame was used when training the refinement module, and when the experiment was conducted by applying it to the comparative model and our model, our model performance improved the most. In  
Table 8 shows the quantitative performance values of the final model we propose.