## 5 Conclusions  
This study proposes an improved method based on the GLCIC algorithm to address the issue of facial image inpainting for Tang Dynasty female terracotta figurines. The algorithm introduces data augmentation, guided filtering, and local enhancement modules to effectively solve the problem of unsatisfactory inpainting results when processing small amounts of data with the traditional GLCIC algorithm. The data augmentation module enhanced the robustness and generalization ability of the model by adding Gaussian noise and other techniques. The guided filtering module uses a guide image to protect facial edge information while smoothing the mottled and damaged areas in the image. The local enhancement module sharpened the image using the Prewitt gradient operator, further highlighting the details of facial features. In analyzing the extent of facial damage in Tang Dynasty female terracotta figurines, we found that approximately 9% of the sample images were evaluated as having minor defects, about 69% were assessed as having moderate defects, and approximately 22% were judged to have severe defects. After applying the improved GLCIC algorithm to restore the faces of these figurines, the experimental results indicated that this algorithm is particularly suitable for images with moderate and minor defects, effectively preserving the edge information and details of facial features. When objectively evaluating the improved GLCIC algorithm, we observed a significant improvement in SSIM, a critical metric. Specifically, most images had SSIM values exceeding 0.8, approaching 0.9, indicating that the restored images have a high structural similarity to the original images, resulting in more realistic and natural restoration effects. However, in terms of PSNR, despite the improved algorithm achieving PSNR values exceeding 20 dB, there was no significant enhancement, and the values did not reach an ideal state. These results suggest that further optimization of the algorithm is needed in future research to improve the texture details and overall consistency of the restored images. In the subjective evaluation part of this study, experts and students were invited to assess the quality of images restored using the improved algorithm. The results showed that in the dimension of shape features, the average scores given by both groups exceeded 4, rated as \"excellent,\" reflecting high accuracy in maintaining the original shape features of the restored images. In the color feature dimension, the average scores ranged between 3 and 4, showing a neutral attitude towards the color restoration effect, suggesting that the accuracy of color restoration in terms of hue, saturation, and value may need further optimization. In the texture feature dimension, the average scores were 1.8 for the expert group and 2.2 for the student group, both below the satisfaction threshold, indicating that the restored images did not meet expectations in terms of texture attributes like coarseness and contrast. These findings reveal the limitations of current image restoration technology in texture recovery and highlight the areas requiring attention and improvement in future research. This study conducted an innovative exploration in the field of facial image restoration for Tang Dynasty female terracotta figurines. There is still room for improvement in shape, color, and texture features, which may be related to factors such as dataset size, model complexity, and algorithm design. In future algorithm improvements, we plan to include more data that capture the shape, color, and texture features of Tang Dynasty female terracotta figurines and enhance the complexity of the algorithms to further improve restoration effects. This study explored new paths and proposed new methods for the protection and preservation of Tang Dynasty female terracotta figurines, expanding the application of facial restoration algorithms in the field of cultural heritage. Conceptualization, Q.F. and C.W. ; methodology, C.W., S.W. and J.X. ; software, S.W.