# Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences
## 4 Results
### Methods Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **SSIM** & **MSE** & **PSNR** & **Sharpness** & **FR Rate (All)** & **FR Rate (\(\cap\))** & **FR Failed** \\ \hline bicubic & 0.806 & 227.196 & 25.654 & 0.084 & 0.476 & 0.473 & 674/2500 \\ bilinear & 0.809 & 217.368 & 25.769 & 0.158 & 0.471 & 0.468 & 693/2500 \\ lanczos & 0.802 & 238.247 & 25.504 & 0.024 & 0.483 & 0.480 & 700/2500 \\ EDSR & 0.780 & 286.999 & 24.687 & \(-\)0.025 & 0.495 & 0.494 & 701/2500 \\ SRCNN & 0.815 & 216.940 & 25.771 & 0.209 & 0.461 & 0.456 & 643/2500 \\ SRGAN & 0.760 & 320.981 & 24.234 & \(-\)0.078 & 0.511 & 0.510 & 771/2500 \\ ESRGAN & 0.788 & 281.192 & 24.604 & \(-\)0.016 & 0.488 & 0.485 & 470/2500 \\ U-Net+GEU & 0.760 & 276.459 & 24.833 & \(-\)0.129 & 0.507 & 0.503 & 317/2500 \\ U-Net+GEU+filter & 0.815 & 234.552 & 25.196 & 0.239 & 0.464 & 0.458 & 394/2500 \\ U-Net+GEU2 & 0.799 & 256.511 & 24.878 & 0.031 & 0.472 & 0.467 & 259/2500 \\ U-Net+GEU3 & 0.799 & 243.433 & 25.135 & 0.004 & 0.471 & 0.466 & 270/2500 \\ U-Net+ResBlock & 0.812 & 239.114 & 25.141 & 0.130 & 0.468 & 0.463 & 581/2500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Single-frame and multi-frame methods performance comparison—mean. As can be seen from Table 3, after a desirable representation of the results and their overall score in form of the sum, U-Net+GEU2 and U-Net+GEU3 methods have a better overall score than the SRCNN method even without using the subjective human evaluation metric (sum1). The overall score including the subjective human evaluation metric (sum2) enlarges the difference between the methods almost twice as much. The U-Net+GEU+filter method has a worse overall score (sum1) than the SRCNN method (1.844 vs. 1.622) but the results are opposite (2.422 vs. 2.600) after including the subjective human evaluation (sum2). An example of resulting images (predictions) on unseen data from all 12 methods is shown in Figure 10. Each row represents one input sequence and each column shows resulting images for each method. Finally, there is a ground truth image. The presented samples are not part of the MLFDB test set because the labels (ground truth images) should not be publicly available, therefore, these sequences were created for presentation purposes only. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **SSIM** & **MSE** & **PSNR** & **Sharp.** & **FR Rate (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{ \mathbf{ \mathbf{ }}}}}}}}}}}\))** & **FR Failed** & **Sub. ev.** & **Sum1** & **Sum2** \\ \hline bicubic & 0.164 & 0.099 & 0.076 & 0.340 & 0.315 & 0.811 & 1 & 1.805 & 2.805 \\ bilinear & 0.109 & 0.004 & 0.001 & 0.655 & 0.222 & 0.848 & 0.989 & 1.839 & 2.828 \\ lanczos & 0.236 & 0.205 & 0.174 & 0.085 & 0.444 & 0.861 & 0.999 & 2.005 & 3.004 \\ EDSR & 0.636 & 0.673 & 0.705 & 0.703 & 0.089 & 0.863 & 0.977 & 3.669 & 4.646 \\ SRCNN & 0 & 0 & 0 & 0.872 & 0 & 0.750 & 0.978 & 1.622 & 2.600 \\ SRGAN & 1 & 1 & 1 & 1 & 0.315 & 1 & 0.995 & 5.315 & 6.310 \\ ESRGAN & 0.491 & 0.618 & 0.759 & 0.051 & 0.537 & 0.412 & 0.844 & 2.868 & 3.712 \\ U-Net+GEU & 0 & 0.572 & 0.903 & 0.532 & 0.870 & 0.113 & 0.570 & 2.990 & 3.560 \\ U-Net+GEU+filter & 0 & 0.169 & 0.374 & 1 & 0.037 & 0.246 & 0.578 & 1.844 & 2.422 \\ U-Net+GEU2 & 0.281 & 0.380 & 0.581 & 0.115 & 0.204 & 0 & 0 & 1.571 & 1.571 \\ U-Net+GEU3 & 0.281 & 0.255 & 0.414 & 0 & 0.185 & 0.021 & 0.204 & 1.166 & 1.370 \\ U-Net+ResBlock & 0.055 & 0.213 & 0.410 & 0.536 & 0.130 & 0.629 & 0.834 & 1.973 & 2.807 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Normalized values comparison of all methods to get the final score. Figure 10: Examples of the resulting methods. All seven 32 \(\times\) 32 px images of the input sequence were used for the multi-frame methods and the 4 th image for single-frame approaches was used. As expected, the interpolation-based methods cannot reconstruct the image in a better quality, but the differences between them are noticeable.