# A Deep Multi-Frame Super-Resolution Network for Dynamic Scenes
## 4 Results
### Comparisons with Other State-of-the-Art Methods  
We compared our method with the following three state-of-the-art algorithms, DUF [17], SOFVSR [16] and RRN [21]. DUF and RRN are two typical implicit motion compensation algorithms, which use 3D convolutional and the recurrent neural network to capture the motion information between frames, respectively. SOFVSR is an optical-flow-based algorithm, which surpasses other optical-flow-based methods. DUF and SOFVSR use 7 and 3 frames to generate one high-resolution frame, respectively. RRN is only capable of handling 2 frames, so it uses 2 frames for evaluation. All the experiments with these state-of-the-art methods were conducted using the corresponding code officially released by their authors. To ensure a fair comparison, we used 2 and 3 frames in our method, which means 1 high-resolution output was computed by its corresponding low-resolution frame together with 1 and 2 adjacent frames, respectively. It should be mentioned that, as for DUF, we did not use the official low-resolution input frames to evaluate because we found huge misalignments between the restoration and the ground truth. Therefore, we used the downsampling function in their code to generate low-resolution frames for evaluation.  
The quantitative comparison results are shown in Table 1, where Ours-2 and Ours-3 represent our method with 2 and 3 input frames, respectively. It was obvious that our method could process different numbers of input frames, and it outperformed other methods in terms of both the PSNR and runtime metrics on the REDS dataset.  
Compared to SOFVSR, an optical-flow-based method using 3 input frames, our method with the same inputs (Ours-3) had an improvement of 0.28 db on PSNR. This  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Bicubic** & **DUF [17]** & **SOFVSR [16]** & **RRN [21]** & **Ours-2** & **Ours-3** \\ \hline PSNR (db) & 25.76 & 26.13 & 28.17 & 28.28 & 28.39 & 28.45 \\ Runtime (s) & - & 1.26 & 0.21 & 0.37 & 0.09 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison on the realistic and dynamic scenes (REDS) dataset.  
proves that the performance gain of our method comes from the fusion strategy proposed in this paper instead of the optical flow approach itself. It may be because that SOFVSR only concentrates on the accuracy of optical flow, while our method focuses on the temporal fusion approach, leading to better results.  
RRN had better performance than SOFVSR and DUF, which indicated that the 3D convolutional layer in RRN learns meaningful temporal information in the super-resolution task. Even so, the value of RRN's PSNR was still a bit lower than that of our method with 2 input frames. This means our fusion approach could obtain promising results.  
As for the computational complexity, we computed an average runtime for 100 samples. As shown in Table 1, our method had the fastest runtime. We believe that the fast speed was attributed to the lightweight of the body part and the fusion part in our network.  
In addition, we show the visual comparisons of the results on the REDS dataset, as shown in Figure 5. Our results were sharper and had fewer artifacts than other methods.  
Figure 5: Visual comparison of the results on the REDS dataset. The images on the left are the results of our method. The zoomed images on the right side are the results of various methods for the area in the bounding box(The Korean in the third figure means ’busy’).