# Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming
## 4 Experiments
### Image Restoration Results  
CNN deep learning was performed after MGEP preclassification. All convolutional layer filters were \(3\times 3\) in size and the number of filters was 64. We used the method of [PERSON] et al. [25] to initialize the convolutional layer. The convolution kernel moved in steps of 1. In order to keep the size of all feature maps the same as the input of each layer, 0 was filled around the boundary before applying the convolution. The learning rate of all layers was initialized to \(5\times 10^{-4}\), and the learning rate dropped by 2 times every 15 epochs until the learning rate was less than \(5\times 10^{-9}\). Tables 1-4 show the PSNR/SSIM values and running times of the six algorithms on the Set5, Set14, BSD100, and Urban100 test sets when the upscale factors were 2, 3, and 4, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Bicubic** & **SRCNN [5]** & **SCN [9]** & **VDSR [8]** & **DRCN [10]** & **MGEP-SRCNN** \\ \hline Set5 & 33.66/0.9299 & 36.66/0.9542 & 36.93/0.9552 & 37.53/0.9587 & 37.63/0.9588 & 37.78/0.9594 \\ Set14 & 30.24/0.8688 & 32.42/0.9063 & 32.56/0.9074 & 33.03/0.9124 & 33.04/0.9118 & 33.29/0.9135 \\ BSD100 & 29.56/0.8431 & 31.36/0.8879 & 31.40/0.8884 & 31.90/0.8960 & 31.85/0.8942 & 32.01/0.8979 \\ Urban100 & 26.88/0.8403 & 29.50/0.8946 & 29.50/0.8960 & 30.76/0.9140 & 30.75/0.9133 & 31.19/0.9181 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average Peak Signal-to-Noise Ratio (PSNR)/Structural Similarity (SSIM) values for 2\(\times\) scale. Red color indicates the best and the blue color indicates the second-best performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Bicubic** & **SRCNN [5]** & **SCN [9]** & **VDSR [8]** & **DRCN [10]** & **MGEP-SRCNN** \\ \hline Set5 & 30.39/0.8682 & 32.75/0.9090 & 33.10/0.9144 & 33.66/0.9213 & 33.82/0.9226 & 34.04/0.9250 \\ Set14 & 27.55/0.7742 & 29.28/0.8209 & 29.41/0.8238 & 29.77/0.8314 & 29.76/0.8311 & 29.97/0.8333 \\ BSD100 & 27.21/0.7385 & 28.41/0.7863 & 28.50/0.7885 & 28.82/0.7976 & 28.80/0.7963 & 28.92/0.7972 \\ Urban100 & 24.46/0.7349 & 26.24/0.7989 & 26.21/0.8010 & 27.14/0.8279 & 27.15/0.8276 & 27.24/0.8330 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average PSNR/SSIM values for 3\(\times\) scale. Red color indicates the best and the blue color indicates the second-best performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Bicubic** & **SRCNN [5]** & **SCN [9]** & **VDSR [8]** & **DRCN [10]** & **MGEP-SRCNN** \\ \hline Set5 & 28.42/0.8104 & 30.48/0.8628 & 30.86/0.8732 & 31.35/0.8838 & 31.53/0.8854 & 31.66/0.8899 \\ Set14 & 26.00/0.7027 & 27.49/0.7503 & 27.64/0.7578 & 28.01/0.7674 & 28.02/0.7670 & 28.19/0.8359 \\ BSD100 & 25.96/0.6675 & 26.90/0.7101 & 27.03/0.7161 & 27.29/0.7251 & 27.23/0.7233 & 27.34/0.7238 \\ Urban100 & 23.14/0.6577 & 24.52/0.7221 & 24.52/0.7250 & 25.18/0.7524 & 25.14/0.7510 & 25.21/0.7837 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average PSNR/SSIM values for 4\(\times\) scale. Red color indicates the best and the blue color indicates the second-best performance. As can be seen from Tables 1-3, the MGEP-SRCNN algorithm achieved the best PSNR effect at different magnifications on the four test sets. Compared with SRCNN, the PSNR evaluation index increased by 0.44-1.69 dB, and the improvement effect obtained in the Set5 dataset was the best. In terms of SSIM indicators, except for the suboptimal data obtained under the 2\(\times\) and 3\(\times\) conditions on the BSD100 dataset, other SSIM indicators were all optimal. Compared with SRCNN, the SSIM evaluation index improved by about 0.005-0.062, where 4\(\times\) showed the best improvement. In addition, from Table 4 we can see that the MGEP-SRCNN algorithm achieved the best performance of running time on the precondition of accuracy.