## 3 Results and Discussion
### Objective Evaluation
Looking at the standard deviations, the usage of different types of temples has no big influence. Considering Table 2 values, the performance of the activation functions worsens in this order: ReLU (bolded for being the best), Mish, Leaky ReLU, and Swish. The values in Table 3 are subtracted from the reference values obtained by the similarity between images obtained in Table 2, resulting in the values in Table 4. Again, we show the average values and standard deviations. Although grade 3 shows an improvement concerning the baseline, it has a larger improvement interval, while grade 1 is affected by the artifacts generated by showing a greater resemblance between the ruined image and the real image. The standard deviations are very low, which means that the different types of temples in the dataset do not pose a problem for the reconstruction. As in degree 1, all the values are negative. This means that all the generated images have worsened the baseline due to the creation of artifacts. Therefore, we can rank the performance of the activation functions in terms of artifact generation from best to worst for grade 1 as ReLU, Mish, Leaky ReLU, and Swish. For the rest of the grades, we find that these differences are not so evident, although ReLU is the one with the best performance index in all of them. This order coincides with that obtained in the previous table. Due to the problem with artifacts in the generated images, we have concluded that evaluation of the entire image is not sufficient. To evaluate the reconstruction of the architectural elements, we have designed an experiment based on the application of a mask that hides the matching pixels so that we can evaluate only those reconstructed elements and, therefore, obtain a complementary. Using this assessment, we focus on measuring how well the missing architectural elements of a temple are reconstructed, which is the main goal of this work. To apply the mask, we find the pixels that have the same value in the image generated with the reconstructed temple and in the real image with the complete temple. All these pixels are assigned black and will not be taken into account when calculating the SSIM. It should be noted that not only the reconstructed area of the temples will be isolated but also other elements, such as shadows, stones, etc., that do not match the real image. In this case, it is not necessary to use a baseline. So, the following table measures the SSIM between the reconstructed temples and the image with the complete temples but uses, in both cases, the mask trying to isolate the area, obtaining the missing elements to be reconstructed. In Table 5, the average results and standard deviations are compiled, and those corresponding to the activation function that performs the best are bolded. Looking at the results of Table 4, we confirm that it is easier to reconstruct missing elements in the case of degree 1, and it gets more difficult while augmenting the area of missing elements. The standard deviations do not change much, which means that there are no big differences between the performances regarding the perspectives and the application to different types of temples. Even though the differences, in this case, are not \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activation Function** & **Degree 1** & **Degree 2** & **Degree 3** \\ \hline ReLU & **67.56\% \(\pm\) 3.31** & **58.75\% \(\pm\) 4.56** & **53.60\% \(\pm\) 6.78** \\ Leaky ReLU & 63.05\% \(\pm\) 4.90 & 55.33\% \(\pm\) 4.67 & 51.21\% \(\pm\) 7.28 \\ Swish & 62.35\% \(\pm\) 4.73 & 55.08\% \(\pm\) 5.02 & 52.26\% \(\pm\) 6.97 \\ Mish & 63.86\% \(\pm\) 5.17 & 56.18\% \(\pm\) 4.78 & 52.82\% \(\pm\) 6.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Masked evaluation of four trainings using SSIM. very high, this evaluation seems to confirm the order of performance for the activation functions ReLU, Mish, Leaky ReLU, and Swish, which becomes more evident in the case of degree 1.