# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 5 Discussion  
Qualitatively assessing the fidelity of our results in accordance with human visual perception is fundamental. Figures 2 and 3 showcase the visual comparisons between different methods, providing insights into their strengths and limitations. The Criminisi method [1] exhibits evident color discrepancies in texture areas, even in the completion of low-resolution damaged images. This limitation stems from the drawbacks of the patch-based synthesis, affecting color consistency. The Laplacian approach [32] addresses the unknown area using a Laplacian pyramid but falls short in color consistency and results in blurriness. EdgeConnect [27], while innovative, exhibits issues such as bright colors from miscalculations and opaqueness. Our proposed method excels in several aspects. The integration of Weighted Laplacian Synthesis and patch-based completion across multi-regions ensures precise and targeted completion, outperforming existing methods. The comparison highlights our method's superiority in artifact reduction and minimizing blurriness, addressing challenges in color discrepancies observed in texture areas. Additionally, the incorporation of pyramid blending proves advantageous, ensuring smoother transitions and preventing noticeable seams or artifacts in blended results. Quantitative evaluation is performed using two perceptual metrics: Learned Perceptual Image Patch Similarity (LPIPS) [34] and Deep Image Structure and Texture Similarity (DISTS) [35]. Tables 1 and 2 present the accuracy of these metrics for different methods and scenarios. Our method consistently outperforms Criminisi [1], Laplacian [32], and EdgeConnect [27] across both high and low resolutions. In terms of LPIPS, our method achieves significantly higher accuracy, indicating better perceptual similarity with ground truth images. The DISTS metric further supports our method's superiority, emphasizing its ability to synthesize diverse texture patterns and maintain structural quality. Moreover, our proposed method applies advanced techniques to address challenges in both high and low-resolution scenarios. In high-resolution paintings, the integration of Laplacian and upsampled Gaussian pyramids progressively enhances completion quality. Texture synthesis employing Laplacian of a Gaussian pyramid at different levels ensures improved edge awareness. The nearest neighbor search algorithm is enhanced to consider potential overlap in color, contributing to more accurate completion decisions. In low-resolution paintings, our method showcases versatility in handling randomly irregular missing regions. The comprehensive approach, from Weighted Laplacian Synthesis to patch-based completion and pyramid blending, demonstrates the robustness of our method across different resolutions. Furthermore, our methodology demonstrates a versatile capability, efficiently handling an array of source media types. This inclusivity spans photography, digitized images, and media featuring intricate textures and structures commonly encountered in art paintings. The algorithm's adaptability stands out as a significant strength, guaranteeing its effectiveness across a wide spectrum of applications. Its consistent efficiency in managing diverse source media not only streamlines workflows but also heightens its utility in digitization and restoration processes. This adaptability underscores the algorithm's potential to make a substantial impact on the comprehensive preservation and restoration of visual content across various mediums. In response to these observations, our approach serves as a valuable guide for curators and artists alike. The insights gleaned from the algorithm's performance highlight the importance of thoughtful decision-making in the creation of art paintings. For instance, the guidance provided can inform choices related to color selection, brushstrokes, and various other elements of the artistic process.