# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 1 Introduction
[5] integrated style transfer techniques, aligning inpainting results more closely with the artistic characteristics of the original painting. Nevertheless, challenges may emerge when transferring highly specific or impressionist artistic styles, demanding careful consideration for optimal performance. These considerations collectively emphasize the dynamic landscape of deep learning methods in art painting completion, highlighting the ongoing need for refinement to address the subtle complexities of diverse artistic compositions. The challenges are particularly pronounced when dealing with impressionist artistic styles, including complicated structures and textures. These intricacies become more apparent in high-resolution images, where the details of brushstrokes are crucial. Our method addresses this challenge by utilizing a pyramid restoration approach that involves the Gaussian pyramid for analyzing base structures and the Laplacian pyramid to enhance edge awareness. This tailored strategy ensures a more effective handling of intricate details in art paintings with impressionist styles, showcasing the adaptability and precision of our approach in diverse contexts. In advancing beyond persistent challenges, our innovative methodology contributes to the field by presenting a comprehensive solution to rectify damages in high-resolution art painting completion, with a specific emphasis on addressing torn and worn-out areas featuring holes. The introduction of the Weighted Similarity-Confidence Laplacian Synthesis algorithm significantly enhances the generation of consistent structure and texture during the reconstruction of missing regions. This forward-looking approach not only yields satisfactory results with a single input image but also ensures the comprehensive digital completion and preservation of art paintings in high resolution. Importantly, this methodology effectively surpasses the limitations associated with physical completion within the confines of museum contexts. The distinct contributions of our work can be summarized as follows:  
* Integrated Solution for Damaged Areas: Our methodology provides a comprehensive solution for repairing damage in high-resolution art painting completion, particularly addressing torn and worn-out areas with holes. * Weighted Similarity-Confidence Laplacian Synthesis: The introduction of this algorithm contributes significantly to the generation of consistent structure and texture, enhancing the reconstruction process for missing regions. * Digital Completion and Preservation: Our forward-looking approach ensures not only satisfying results with a single input image but also guarantees the comprehensive digital completion and preservation of art paintings in high resolution. * Surpassing Physical Limitations: Importantly, our methodology surpasses the limitations associated with physical completion within museum contexts, offering a more versatile and effective solution for art restoration and preservation. The subsequent sections of this paper are structured as follows. We commence with a discussion on related work in Section 2. Following that, we introduce our proposed method in Section 3. The experimental results are showcased in Section 4, and we delve into the implementation details in Section 5. Finally, we provide a comprehensive conclusion and outline potential future work in Section 6.