# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 6 Conclusions  
In conclusion, our proposed methodology, anchored in Weighted Laplacian Synthesis and patch-based completion across multi-regions, emerges as a robust solution for the complicated task of restoring art paintings with irregularly damaged regions. Through a comprehensive evaluation, including qualitative and quantitative comparisons, our approach showcased its superiority over existing methods. Notably, the integration of hierarchical pyramid blending and advanced nearest neighbor search algorithms contributed to artifact reduction, minimized blurriness, and ensured visually pleasing outcomes in both high and low-resolution art paintings. The comprehensive calibration of perceptual similarity, as demonstrated by the Learned Perceptual Image Patch Similarity (LPIPS) metric, and the synthesis of diverse texture patterns through Deep Image Structure and Texture Similarity (DISTS) further validated the efficacy of our method. Our proposed approach not only addresses the limitations observed in current methodologies but also establishes a new standard for art completion algorithms, particularly in handling complex structures and textures. While our current methodology stands as a significant advancement, future work could explore the incorporation of depth considerations in art paintings. This involves a more detailed examination of damaged areas within complicated structures and textures, taking into account the intricacies of brushstrokes and other artistic elements. By enhancing our algorithm to analyze the depth of art paintings, we aim to provide a more nuanced and detailed approach to restoring damaged regions, offering a higher level of precision. This depth-aware analysis can contribute to a more accurate understanding of the damaged areas, especially in complicated art styles where brushstrokes contribute significantly. By delving into the third dimension of art, our algorithm could discern variations in depth, leading to improved restoration outcomes. This approach not only adds an extra layer of \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Criminisi [1]**} & \multicolumn{2}{c}{**Laplacian [32]**} & \multicolumn{2}{c}{**EdgeConnect [27]**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-9}  & **High** & **Low** & **High** & **Low** & **High** & **Low** & **High** & **Low** \\ \hline
**Girl** & 0.631 & 0.443 & 0.701 & 0.562 & 0.702 & 0.344 & **0.846** & **0.812** \\ \hline
**Man** & 0.642 & 0.531 & 0.719 & 0.407 & 0.745 & 0.307 & **0.897** & **0.808** \\ \hline
**Scenery** & 0.611 & 0.472 & 0.754 & 0.592 & 0.762 & 0.412 & **0.853** & **0.781** \\ \hline
**Woman** & 0.714 & 0.523 & 0.758 & 0.575 & 0.781 & 0.635 & **0.892** & **0.852** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of Learned Perceptual Image Patch Similarity (LPIPS). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Criminisi [1]**} & \multicolumn{2}{c}{**Laplacian [32]**} & \multicolumn{2}{c}{**EdgeConnect [27]**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-9}  & **High** & **Low** & **High** & **Low** & **High** & **Low** & **High** & **Low** \\ \hline
**Girl** & 0.721 & 0.543 & 0.899 & 0.661 & 0.895 & 0.618 & **0.951** & **0.786** \\ \hline
**Man** & 0.792 & 0.601 & 0.872 & 0.644 & 0.834 & 0.562 & **0.932** & **0.888** \\ \hline
**Scenery** & 0.710 & 0.592 & 0.888 & 0.691 & 0.842 & 0.512 & **0.921** & **0.834** \\ \hline
**Woman** & 0.812 & 0.611 & 0.878 & 0.655 & 0.852 & 0.615 & **0.932** & **0.891** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of Deep Image Structure and Texture Similarity (DISTS). sophistication to the algorithm but also aligns with the intricacies of artistic expression. Furthermore, exploring the integration of machine learning techniques to adaptively adjust parameters based on variations in image content can significantly enhance the algorithm's flexibility and adaptability.