# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 1 Introduction  
The integration of image completion in the realm of art painting restoration is a central focus in contemporary academic exploration within computer vision and image processing. Image completion, or inpainting, a method aimed at restoring missing or damaged segments in an image, serves as a fundamental element in mitigating the impacts of physical deterioration experienced by art paintings in museums, encompassing issues like scratches, tears, and various forms of degradation. In situations where manual completion methods encounter limitations, the digitization of art paintings into high-resolution images has emerged as a viable alternative. When applied to digital images or paintings, high resolution implies an increased number of pixels per unit of area, resulting in a more refined and detailed representation of artistic elements. In the proposed algorithm for art painting completion, achieving high resolution means ensuring that the completed art painting conserves and reproduces complicated details, nuanced textures, and subtle artistic expressions with remarkable fidelity to the original. Moreover, the shift from physical to digital preservation introduces unique challenges, often compounded by the constraints inherent in established completion methods. Standard approaches, reliant on manual techniques and color matching, encounter difficulties in capturing the complicated details of artistic compositions. This challenge becomes evident in potential distortions, unnatural colorations, and a diminished preservation of the original aesthetic essence when compared to the capabilities offered by advanced inpainting techniques. Furthermore, when dealing with high-resolution images, a common necessity in the completion of art paintings, the intricacies of small-scale texture synthesis and the precise inference of missing information from distant contexts become particularly pronounced. The demand for increased precision and fidelity in the inpainting process for high-resolution art paintings adds an additional layer of complexity to the completion endeavor. Consequently, the ongoing exploration of effective inpainting methodologies for high-resolution art paintings underscores the critical necessity for advancements in computer vision and image processing. This is imperative for addressing the distinctive challenges posed by the elaborate textures and structures inherent in artistic masterpieces. Such research endeavors are essential for ensuring the comprehensive digital completion and preservation of art paintings, surpassing the limitations of physical completion within museum contexts. In the field of art painting completion, traditional image inpainting methods have significantly contributed to the completion of damaged or missing regions. Patch-based methods, exemplified by [PERSON] et al. [1] and [PERSON] et al. [2], have proven effective but face challenges in faithfully replicating complicated artistic structures, such as brush strokes and fine textures. The drawbacks include potential distortions and a loss of aesthetic fidelity when applied to varied textures in art paintings. Moreover, [PERSON] et al. [3] faced challenges in merging art paintings with diverse styles or complex compositions. Recognizing these drawbacks underscored the imperative for tailored advancements in inpainting methods to address the unique intricacies of art painting completion. In enhancing art painting completion through deep learning, several approaches have been explored, each with distinctive advantages and inherent challenges. [PERSON] et al. [4] effectively captured complicated textures and structures, yet they faced difficulties when inpainting areas with highly complicated artistic details, potentially resulting in less accurate replication. However, its application might be limited when faced with irregular structures or unconventional color schemes in diverse art paintings. [PERSON] et al.