# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 4 Experimental Results
### Qualitative Comparison  
In examining the fidelity of our results in accordance with human visual perception, we utilize a qualitative comparison to scrutinize the complicated details of structures and textures. Particular emphasis is placed on Figures 2 and 3, pertaining to the genre art painting of impressionist landscapes (Image Scenery) and impressionist portraits (Image Girl, Man, and Woman). This comparative analysis is designed to clarify the noticeable differences and advancements realized through our approach in contrast to earlier methodologies.  
In Figure 2, the limitations of the Criminisi method [1] become apparent, revealing evident color discrepancies in the texture area. This issue persists even in the completion of low-resolution damaged images, as highlighted in Figure 3. The root cause of this problem lies in the drawbacks of the patch-based synthesis following the construction of structure propagation. The method heavily relies on image isophotes, significantly impacting the prioritization of filling and resulting in suboptimal color consistency in the final results.  
Meanwhile, the Laplacian approach [32] proposes a strategy involving the construction of a Laplacian pyramid to address the unknown area. This method systematically examines consistent patch similarity at each level and resolves the issue through a weighted vote of scales. However, a notable limitation arises from its exclusive focus on the target area of the current level for filling the missing region. This singular approach contributes to significant color inconsistencies and blurriness, as observed in all images depicted in Figures 2 and 3.  
In contrast, the deep learning method EdgeConnect [27] takes a distinct approach by utilizing edge-map guidance to predict a model for recovering missing regions. However, despite its innovative strategy, limitations become apparent in the form of bright colors resulting from miscalculations and opaqueness across all presented results in Figures 2 and 3. These issues appear from long-term memorization and an insufficient model derived from a less extensive dataset of painting images.  
Furthermore, our proposed method excels over other approaches based on several key aspects of the proposed methodologies. Firstly, the integration of Weighted Laplacian Synthesis and patch-based completion across multi-regions allows our method to provide more precise and targeted completion. This approach ensures a focused completion process, addressing specific regions with a higher level of accuracy. Secondly, the comparison with existing methods, such as Criminisi method [1], Laplacian approach [32], and EdgeConnect [27] highlights the superiority of our approach in terms of artifact reduction and minimizing blurriness, while these existing methods struggle with color discrepancies,particularly in texture areas, our method exhibits a notable improvement in maintaining color consistency and sharpness.  
In addition, our method employs the advantages of pyramid blending, offering a more effective strategy for combining or blending images. The hierarchical structure of pyramids ensures smoother transitions, preventing noticeable seams and artifacts in the blended results. This is an advantage when dealing with images featuring diverse content or structures.