# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 4 Experiment
### Comparison on Self-Ensemble Results  
Self-ensemble is an effectively way to take advantage of the augmented data. Since EDSR [4] and RDN [7] also support self-ensemble, we make a visual comparison among these methods. As shown in Figure 8 the red boxes mark the detail of the ground truth and the results of img_092 from Urban100 [12] with TAN+, EDSR+ [4], and RDN+ [7]. It can be seen that under data augmentation, since TAN+ can make better use of more information under different receptive fields, better results can be obtained than EDSR+ and RDN+. Both EDSR+ and RDN+ had serious errors in reconstructing high-resolution images, even though they were able to get relatively sharp lines. In addition, the distortion of TAN+ on smaller pixel scales is also better controlled.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Scale** & **SRCNN** & **LapSRN** & **MemNet** & **EDSR** & **RDN** & **TAN (Ours)** & **TAN + (Ours)** \\  & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Set5 & \(\times\)2 & 36.65/0.955 & 37.52/0.959 & 37.78/0.960 & 38.11/0.960 & 38.24/0.962 & 38.30/0.964 & 38.31/0.965 \\  & \(\times\)4 & 30.50/0.863 & 31.54/0.885 & 31.74/0.889 & 32.46/0.897 & 32.47/0.899 & 32.34/0.901 & 32.41/0.896 \\  & \(\times\)8 & 25.33/0.690 & 26.15/0.738 & 26.16/0.741 & 26.96/0.776 & ---- & 26.79/0.772 & 26.84/0.773 \\ Set14 & \(\times\)2 & 32.29/0.908 & 33.08/0.913 & 33.28/0.914 & 33.92/0.920 & 34.01/0.921 & 33.98/0.924 & 34.07/0.925 \\  & \(\times\)4 & 27.52/0.753 & 28.19/0.772 & 28.26/0.772 & 28.80/0.786 & 28.81/ 0.787 & 28.78/0.795 & 28.82/0.796 \\  & \(\times\)8 & 23.76/0.591 & 24.35/0.620 & 24.38/0.620 & 24.91/0.642 & ---- & 25.04/0.659 & 25.08/0.661 \\ BSD & \(\times\)2 & 31.36/0.887 & 31.80/0.895 & 32.08/0.898 & 32.32/0.901 & 32.34/0.901 & 32.37/0.906 & 32.41/0.907 \\
100 & \(\times\)4 & 26.91/0.712 & 27.32/0.727 & 27.40/0.728 & 27.71/0.742 & 27.72/0.742 & 27.62/0.748 & 27.66/0.741 \\  & \(\times\)8 & 24.13/0.566 & 24.54/0.586 & 24.58/0.584 & 24.81/0.598 & ---- & 24.80/0.614 & 24.84/0.615 \\ Urban & \(\times\)2 & 29.23/0.895 & 30.41/0.910 & 31.31/0.920 & 32.93/0.935 & 33.09/ 0.937 & 33.04/0.938 & 33.13/0.939 \\
100 & \(\times\)4 & 24.53/0.725 & 25.44/0.756 & 25.50/0.763 & 26.64/0.804 & 26.62/0.803 & 26.61/0.808 & 26.73/0.809 \\  & \(\times\)8 & 21.29/0.544 & 21.81/0.581 & 21.89/0.583 & 22.51/0.622 & ---- & 22.74/0.645 & 22.83/0.648 \\ Manga & \(\times\)2 & 35.82/0.969 & 37.27/0.974 & 37.72/0.974 & 39.10/0.977 & 39.18/0.978 & 39.42/0.981 & 39.59/0.981 \\
109 & \(\times\)4 & 27.83/0.866 & 29.09/0.890 & 29.42/0.894 & 31.02/ 0.915 & 31.00/0.915 & 30.88/0.918 & 31.04/0.919 \\  & \(\times\)8 & 22.46/0.695 & 23.39/0.735 & 23.56/0.738 & 24.69/ 0.784 & ---- & 24.67/0.796 & 24.80/0.799 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compare with state-of-the-art methods. The best result is marked with red and the second is marked with blue.  
Figure 7: Performance and parameter comparison of scale \(\times\) 2 reconstructed for images in Set5. TAN shows the best PSNR with relatively lower model parameters compared with those deep learning-based methods LapSRN [5], MemNet [8], RED [28], DBPN [27], EDSR [4] and RDN [7].