Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion

Abstract

Artistic image completion assumes a significant role in the preservation and restoration of invaluable art paintings, marking notable advancements through the adoption of deep learning methodologies. Despite progress, challenges persist, particularly in achieving optimal results for high-resolution paintings. The intricacies of complex structures and textures in art paintings pose difficulties for sophisticated approaches like Generative Adversarial Networks (GANs), leading to issues such as small-scale texture synthesis and the inference of missing information, resulting in distortions in lines and unnatural colors. Simultaneously, patch-based image synthesis, augmented with global optimization on the image pyramid, has evolved to enhance structural coherence and details. However, gradient-based synthesis methods face obstacles related to directionality, inconsistency, and the computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a pioneering approach, integrating Weighted Similarity-Confidence Laplacian Synthesis to comprehensively address these challenges and advance the field of artistic image completion. Experimental results affirm the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with intricate details and irregular missing regions. The integration of weighted Laplacian synthesis and patch-based completion across multi-regions ensures precise and targeted completion, outperforming existing methods. A comparative analysis underscores our method’s superiority in artifact reduction and minimizing blurriness, particularly addressing challenges related to color discrepancies in texture areas. Additionally, the incorporation of pyramid blending proves advantageous, ensuring smoother transitions and preventing noticeable seams or artifacts in blended results. Based on empirical results, our method consistently outperforms previous methods across both high and low resolutions. Responding to these insights, our approach emerges as an invaluable guide for both curators and artists. The algorithm’s performance yields insights that underscore the central role of thoughtful decision making in the creation of art paintings. This guidance extends to informing choices related to color selection, brushstrokes, and various other elements integral to the artistic process. During the creation phase, employing these insights enables artists and curators to optimize not only the digitization but also the subsequent restoration process. This proves especially vital when dealing with the intricacies involved in physically restoring damaged original art paintings. Importantly, our approach not only streamlines the restoration process but also contributes significantly to the preservation and enhancement of the digital representations of these distinctive and often intricate works of art.