## 3 Results and Discussion
### Objective Evaluation  
In the next step, we evaluate the different activation functions as an objective evaluation based on mathematical metrics. To this end, we provide the SSIM between the real image and the synthetic one (Table 3) and then the difference between the values of this table and the ones in Table 1, which contains the baseline (Table 4). In all these tables, each row corresponds to one of the direct trainings (depending on the activation function) and the columns to the destruction degree. The results in the cells contain the mean value of all the views with their standard deviation. We have provided the standard deviation in each destruction degree, as perspectives of the temples affect some cases. As this evaluation uses the entire image and not only the missing architectonical elements, the influence of visual artifacts is evaluated. To uncover information about these specific instances, we have a more thorough examination of the reconstructions during the objective assessment. Considering the results in the table above, which shows the average values and standard deviations, we conclude the following. It should be noted that going from grade 1 to grade 3, we find an improvement in the baseline produced by the reconstructed image, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activation Function** & **Degree 1** & **Degree 2** & **Degree 3** \\ \hline ReLU & \(-1.25\%\pm 0.63\) & \(2.17\%\pm 1.54\) & \(3.59\%\pm 1.62\) \\ Leaky ReLU & \(-4.59\%\pm 1.46\) & \(-0.14\%\pm 1.90\) & \(1.79\%\pm 1.87\) \\ Swish & \(-5.22\%\pm 1.24\) & \(-0.61\%\pm 0.77\) & \(1.65\%\pm 1.42\) \\ Mish & \(-3.41\%\pm 1.25\) & \(0.67\%\pm 1.11\) & \(2.56\%\pm 1.55\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Differences between SSIM for the whole image and the baseline for the four trainings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Perspective** & **Degree 1** & **Degree 2** & **Degree 3** \\ \hline Frontal & 95.91\% \(\pm\) 0.87 & 87.11\% \(\pm\) 5.29 & \(77.65\%\pm 7.03\) \\ Front right vertical & 96.68\% \(\pm\) 2.00 & 90.06\% \(\pm\) 5.84 & \(82.10\%\pm 9.47\) \\ Right side & 96.48\% \(\pm\) 3.05 & 90.54\% \(\pm\) 5.60 & \(83.90\%\pm 8.36\) \\ Rear right vertical & 95.22\% \(\pm\) 3.83 & 86.04\% \(\pm\) 9.83 & \(80.05\%\pm 9.71\) \\ Rear & 93.54\% \(\pm\) 3.37 & 81.93\% \(\pm\) 13.04 & \(77.36\%\pm 11.53\) \\ Rear left vertical & 96.58\% \(\pm\) 2.12 & 87.15\% \(\pm\) 9.16 & \(83.83\%\pm 9.77\) \\ Left side & 88.28\% \(\pm\) 0.59 & 91.60\% \(\pm\) 3.75 & \(87.85\%\pm 5.32\) \\ Front left vertical & 98.20\% \(\pm\) 0.83 & 91.17\% \(\pm\) 4.19 & \(84.64\%\pm 6.15\) \\ \hline
**Total** & **96.36\% \(\pm\) 1.44** & **88.2\% \(\pm\) 3.07** & **82.17\% \(\pm\) 3.39** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Baseline measures for SSIM using the whole image. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activation Function** & **Degree 1** & **Degree 2** & **Degree 3** \\ \hline ReLU & **95.09\% \(\pm\) 1.64** & **90.37\% \(\pm\) 1.74** & **85.76\% \(\pm\) 2.01** \\ Leaky ReLU & 91.76\% \(\pm\) 1.61 & 88.06\% \(\pm\) 1.55 & 83.96\% \(\pm\) 1.70 \\ Swish & 90.70\% \(\pm\) 2.24 & 87.59\% \(\pm\) 2.73 & 83.82\% \(\pm\) 2.36 \\ Mish & 92.94\% \(\pm\) 2.25 & 88.88\% \(\pm\) 2.04 & 85.99\% \(\pm\) 3.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluations of four trainings with the whole image using SSIM. which ranges from around 7% to 10%. It is worth emphasizing that in the case of degree 3, the part to be reconstructed is larger, and therefore, there is a greater range of improvement in this image. Concerning degrees 1 and 2 of destruction, we observe that the activation function that incorporates a higher rate of artifacts is Swish, followed by Leaky ReLU, Mish, and ReLU, obtaining worse results for the baseline. Since the values provided do not show a large significant difference, we can deduce that the reconstructions are more complicated in addition to having large artifacts that differ from the real image.