# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 4 Experiments
### Comparisons with the State-of-the-Art Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Scale** & **SRCNN [3]** & **VDSR [4]** & **DRRN [6]** & **SRGAN [22]** & **EGAN** \\ \hline \multirow{3}{*}{Set5} & \(\times\) 2 & 36.66/0.9542/8.04 & 37.53/**0.9587**/8.19 & **37.74**/0.9571/**8.67 & 35.63/0.9418/8.23 & 36.96/0.9553/8.43 \\ \cline{2-8}  & \(\times\) 3 & 32.75/0.9090/4.66 & 33.66/0.9213/5.22 & **34.03**/**0.9244**/5.40 & 31.82/0.8826/4.69 & 33.92/0.9234/**5.47** \\ \cline{2-8}  & \(\times\) 4 & 30.48/0.8628/3.00 & 31.35/0.8838/3.50 & **31.68**/**0.8888**/3.70 & 29.53/0.8304/3.12 & 31.64/0.8798/**3.76** \\ \hline \multirow{3}{*}{Set14} & \(\times\) 2 & 32.45/0.9067/7.79 & 33.03/0.9124/7.88 & **33.23**/**0.9136**/**8.32 & 31.04/0.9018/7.83 & 33.17/0.9123/8.25 \\ \cline{2-8}  & \(\times\) 3 & 29.30/0.8215/4.34 & 29.77/0.8314/4.73 & **29.96**/**0.8349**/4.88 & 28.76/0.8171/4.82 & 29.25/0.8324/**4.99** \\ \cline{2-8}  & \(\times\) 4 & 27.50/0.7513/2.75 & 28.01/0.7674/3.07 & **28.21**/**0.7720**/3.25 & 26.52/0.7470/3.17 & 28.13/0.7602/**3.29** \\ \hline \multirow{3}{*}{BSD100} & \(\times\) 2 & 31.36/0.8879/7.24 & 31.90/0.8960/7.17 & **32.05**/**0.8973**/**7.70** & 30.85/0.8342/7.32 & 31.98/0.8921/7.74 \\ \cline{2-8}  & \(\times\) 3 & 28.41/0.7863/3.37 & 28.82/0.7976/3.94 & **28.95**/0.8004/4.21 & 27.80/0.7363/4.04 & 28.89/**0.8005**/**4.35** \\ \cline{2-8}  & \(\times\) 4 & 26.90/0.7101/2.41 & 27.29/0.7251/2.63 & **27.36**/**0.7284**/2.77 & 25.43/0.6633/2.59 & 27.11/0.7186/**2.82** \\ \cline{2-8}  & \(\times\) 2 & 29.50/0.8946/8.00 & 30.76/0.9140/8.27 & 31.23/0.9188/8.92 & 29.75/0.9033/8.33 & **31.39**/**0.9202**/**9.01** \\ \cline{2-8} Urban100 & \(\times\) 3 & 26.24/0.7989/4.58 & 27.14/0.8279/5.19 & 27.53/0.8076/5.32 & 27.15/0.7076/5.32 & **27.61**/**0.8441**/**5.54** \\ \cline{2-8}  & \(\times\) 4 & 24.52/7.221/2.96 & 25.18/0.7524/3.41 & **25.44**/0.7310/3.68 & 25.34/0.7310/3.51 & 25.40/**0.7639**/**3.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benchmark results. Average PSNR, SSIM, and IFC on the datasets for comparison, increasing by factors of 2, 3, and 4. **Bold fonts** represent the best results for each category. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Algorithms** & **SRCNN [3]** & **VDSR [4]** & **DRRN [6]** & **SRGAN [22]** & **EGAN** \\ \hline
**Datasets** & **P(dB)** & **T(s)** & **P(dB)** & **T(s)** & **P(dB)** & **T(s)** & **P(dB)** & **T(s)** & **P(dB)** & **T(s)** \\ \hline Set5 & 30.48 & 0.25 & 31.35 & 0.15 & 31.54 & 2.30 & 30.91 & 1.82 & 31.53 & 1.12 \\ \hline Set14 & 27.50 & 0.46 & 28.01 & 0.28 & 28.19 & 4.88 & 27.40 & 3.56 & 28.15 & 2.46 \\ \hline BSD100 & 26.90 & 0.22 & 27.29 & 0.17 &Figure 7: Visual comparisons for \(\times\)2, \(\times\)3, and \(\times\)4 SR on Set5, image “woman” with scale factor \(\times\)2, image “baby” with scale factor \(\times\)3, and image “butterfly” with scale factor \(\times\)4 are shown, respectively, in the 3 lines. Figure 8: Visual comparisons for \(\times\)2, \(\times\)3, and \(\times\)4 SR on Set14, image “lenna” with scale factor \(\times\)2, image “ppt3” with scale factor \(\times\)3, and image “baboon” with scale factor \(\times\)4 are shown, respectively, in the 3 lines. Figure 9: Visual comparisons for \(\times\)2, \(\times\)3, and \(\times\)4 SR on BSD100, image “101087” with scale factor \(\times\)2, image “148026” with scale factor \(\times\)3, and image “302008” with scale factor \(\times\)4 are shown, respectively, in the 3 lines. Figure 10: Visual comparisons for x2, x3, and x4 SR on BSD100, image “img009” with scale factor x2, image “img007” with scale factor x3, and image “img077” with scale factor x4 are shown, respectively, in the 3 lines.