# Generative Adversarial Network for Image Super-Resolution Combining Texture Loss
## 4 Experiments and Results
### Experimental Results  
#### 4.2.1 Quantitative Evaluation  
We have performed super-resolution experiments on Set5 and Set14 to analyze the effects of introducing RDB structure, \(L_{tex}\) and improving initial \(L_{adv}\), \(L_{per}\) on super-resolution performance. The PSNR values of different model variants are shown in Table 1. It can be observed that each of the above four enhanced measures can improve the super-resolution performance of the network, and the effect is the best when all of them are used. In addition, we have adopted different values for \(\lambda\) and \(\eta\) in Equation (3) and performed experiments on Set5. The results have shown that the reconstruction effect is the best when \(\lambda=3\times 10^{-3}\) and \(\eta=2\times 10^{-2}\). Table 2 presents the average PSNR results on Set5 dataset.  
For fair comparison, the SISR methods in comparison are Bicubic [4], ScSR [8], SRGAN [17], EDSR [23] and ESRGAN [24], all these methods are tested on Set5, Set14 and BSD100, respectively. Average PSNR/SSIM values on different datasets with those methods are recorded in Table 3, and the total running time with those methods on different datasets is recorded in Table 4. It can be seen from Table 3 that the performance of TSRGAN on PSNR is generally better than other algorithms. Except that the SSIM value is slightly lower than ESRGAN 0.009 on Set14, it is also superior than other algorithms. Note that Table 4 shows the results that Bicubic consumes the shortest time for it only has interpolation operations. ScSR spends longer time for learning sparse representation dictionaries between the LR and HR image patch pairs. SRGAN, EDSR, ESRGAN and TSRGAN models all need longer time to train for they have extensive convolutional layers. SRGAN has the slowest reconstruction speed because the BN layer is not removed in the network structure, while TSRGAN is slightly slower than EDSR and ESRGAN due to the introduction of deeper network and texture loss. Synthesizing Tables 3 and 4, TSRGAN obviously improves PSNR and SSIM indicators for measuring the quality of image reconstruction without losing too much speed, which verifies its effectiveness and superiority.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RDB** & \(L_{adv}\) & \(L_{per}\) & \(L_{tex}\) & **Set5** & **Set14** \\ \hline \(\times\) & \(\times\) & \(\times\) & \(\times\) & 30.37 & 27.02 \\ \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 31.22 & 27.98 \\ \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 31.54 & 28.27 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 31.83 & 28.39 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 32.38 & 28.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average PSNR (dB) of different super-resolution models on Set5 and Set14 datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\eta\)\(\lambda\) & \(1\times 10^{-3}\) & \(2\times 10^{-3}\) & \(3\times 10^{-3}\) & \(4\times 10^{-3}\) & \(5\times 10^{-3}\) \\ \hline \(1\times 10^{-2}\) & 32.31 & 32.34 & 32.36 & 32.35 & 32.36 \\ \(2\times 10^{-2}\) & 32.32 & 32.35 & 32.38 & 32.36 & 32.35 \\ \(3\times 10^{-2}\) & 32.31 & 32.33 & 32.36 & 32.34 & 32.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average PSNR (dB) results on Set5 dataset when \(\lambda\) and \(\eta\) take different values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Set5** & **Set14** & **BSD100** \\ \hline Bicubic & 30.07/0.862 & 27.18/0.786 & 26.68/0.729 \\ ScSR & 30.29/0.868 & 27.69/0.790 & 26.94/0.730 \\ SRGAN & 30.36/0.873 & 27.02/0.772 & 26.51/0.724 \\ EDSR & 31.53/0.882 & 28.02/0.793 & 27.23/0.732 \\ ESRGAN & 32.05/0.895 & 28.49/0.819 & 27.58/0.747 \\ TSRGAN & 32.38/0.967 & 28.73/0.810 & 27.67/0.764 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average PSNR (dB)/SSIM comparison of different SR algorithms on Set5, Set14 and BSD100 datasets.