## 4 Results and Discussion
### Inpainting and Evaluation of Tang Dynasty Female Terracotta Figurine Facial Images  
In this study, to evaluate the generalization ability of the proposed algorithm, we selected a painted terracotta standing female terracotta figurine from the Palace Museum collection as the test object. As shown in Figure 14, the facial features of this standing female terracotta figurine, including the eyes, mouth, and eyebrows, are significantly missing. Based on the previously mentioned calculation of the image damage extent in the study, we can subjectively estimate that the original facial damage extent is 59%, categorized as moderate damage. By applying our algorithm, the generated face image exhibited similarities to the features described in previous studies [32; 74], presenting the classical beauty of the female terracotta figurine with delicate eyebrows, slender eyes, and a small cherry-like mouth. In the generated image, the eyebrows and pupils of the female terracotta figurine are painted black, and the mouth is painted red, recreating the typical makeup of Tang Dynasty terracotta figurines. The facial damage extent of the Tang Dynasty female terracotta figurines was reduced to 29% after restoration, categorizing it as slight damage. The restoration results for the significantly damaged facial areas reached a satisfactory level. Due to the inability to objectively determine the quality of the restoration, this study utilized the ACR method for the subjective evaluation of the restored facial images of Tang Dynasty figurines. A total of 60 questionnaires were collected from cultural heritage students, along with 5 questionnaires from experts in related fields. According to the evaluation results from the cultural heritage students (Table 5), \"Shape\" received the highest average score among the three evaluation dimensions, indicating that students were generally satisfied with the structural integrity of the restored faces. \"Color\" followed with an average score of 3.5, reflecting a more neutral evaluation from the students on this dimension. \"Texture\" received the lowest average score, indicating lower satisfaction with the texture restoration, with a certain proportion of negative evaluations. The standard deviation data show that there was not much divergence in students' opinions across the three dimensions. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GLCIC** & \multicolumn{2}{c}{**Improved GLCIC Algorithm for Tang Female Terracotta Figurines**} \\ \hline \multirow{2}{*}{Figure 10a} & SSIM & 0.6278 & 0.8698 \\ \cline{2-4}  & PSNR & 19.3795 & 21.3070 \\ \hline \multirow{2}{*}{Figure 10b} & SSIM & 0.3785 & 0.8787 \\ \cline{2-4}  & PSNR & 14.0642 & 27.5678 \\ \hline \multirow{2}{*}{Figure 10c} & SSIM & 0.4443 & 0.8075 \\ \cline{2-4}  & PSNR & 15.4271 & 21.4724 \\ \hline \multirow{2}{*}{Figure 10d} & SSIM & 0.6766 & 0.5536 \\ \cline{2-4}  & PSNR & 16.8169 & 20.5920 \\ \hline \multirow{2}{*}{Figure 10e} & SSIM & 0.7435 & 0.8947 \\ \cline{2-4}  & PSNR & 22.4800 & 25.9771 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SSIM and PSNR of the two algorithms compared to the original images. The results of the expert questionnaire (Table 6) show that the shape received the highest ratings, with most experts giving it an \"excellent\" evaluation. The color features followed, with most experts giving it a \"medium\" evaluation. Finally, texture features received the lowest ratings, which included several \"fair\" evaluations. The standard deviation data indicate that there was little divergence in the experts' opinions across the three dimensions. The Pearson correlation coefficient analysis between the two subjective indicators shows a strong positive correlation between the student evaluations and expert evaluations (PCC = 0.997, significance level \(p<0.05\)). This indicates that the evaluation results from students and experts are convergent, and the assessment results are reliable.