# Video Super Resolution Using a Selective Edge Aggregation Network
## 4 Experiment
### Refinement  
We defined some basic conditions for systematic experiments and proceeded with step-by-step experiments. First, in the process of determining the final refinement module, Vimeo90k is used as the training dataset and Vid4 is used as the test dataset. Second, the base model layer used between experiments used 3D convolution as shown in Figure 5. The procedure for conducting the experiment is as follows. First, we verify the parameter values of \"Canny edge detection\", which has the best performance in our model. Second, the results according to various model structures are checked. Third, after selecting the final model, the form of input data is newly proposed and tested during training. Finally, the necessity of the refinement module is shown by applying the proposed refinement to several models. When testing the sigma value of Canny edge detection and experimenting with various model structures, the refinement module learned HR through bicubic upsampling. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Baseline** & **Detect Blur** & \(Loss_{m}\) & **Refinement** & **PSNR/SSIM** \\ \hline \(\surd\) & & & & 30.15/0.8595 \\ \(\surd\) & \(\surd\) & & & 30.26/0.8648 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 30.40/0.8670 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 32.32/0.9069 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation study of our method and loss function on REDS4 dataset. “Baseline” means without using proposed method and \(Loss_{m}\). “Detect Blur” represents blur frame detection before edge map extraction. “Refinement” means proposed method in Section 3.5. \(Loss_{m}\) is the proposed magnitude loss function. As you can see in Table 2, we found the optimal sigma value. In \"Canny edge detection\", the value \"Sigma\" determines the strength of the edge. We test the sigma values from 0.5 to 4 on the same picture. As a result, the best performance of PSNR and SSIM are obtained at sigma = 0.7 and 1. However, when comparing the two scores, since the difference in PSNR is larger than the difference in SSIM, we conduct all subsequent experiments based on the value of 0.7. We conduct four additional experiments on the model structure. To reduce the loss as much as possible, like the base model in Figure 6, we try to keep the 3D features of the same size without going through the compression and recovery process. We will refer to this experimental method as \"VDLayer\". This experiment is conducted to maintain the structural information of the data image as much as possible, and the optimal layer is found by setting the number of layers to 15, 19, and 23. As you can see in Table 3, VDLayer = 19 is the highest for SSIM, but for PSNR, the existing base model performs the best. VDLayer is helpful in maintaining the structural shape of the image as expected, but learning is not smooth due to the rather large number of parameters, and accordingly, it was difficult to expect an increase in PSNR. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sigma Value** & **PSNR** & **SSIM** \\ \hline
0.5 & 26.49 & 0.8009 \\
0.7 & 27.52 & 0.8178 \\
1 & 27.34 & 0.8175 \\
2 & 27.06 & 0.8141 \\
3 & 26.65 & 0.8080 \\
4 & 26.48 & 0.7980 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results according to sigma value of Canny edge detection in refinement base models. Figure 5: The architecture of the basic model used in the refinement experiments. The input data is an RGB image and the edge information is a pair. The first 7 frames are subjected to 3D convolution to reduce the channel and size. In the same way, the channel is reduced to 5 frames and then reduced to 3 frames. The data for 3 frames is 3D convolution using zero padding and goes through 9 layers without changing the size. Afterward, restore it in the opposite way to the way it was reduced. Figure 6: Using VDLayer’s architecture that does not compress and recover input data, we test performance changes according to various model structures.