Version 1
: Received: 17 January 2024 / Approved: 17 January 2024 / Online: 17 January 2024 (12:07:28 CET)
Version 2
: Received: 25 February 2024 / Approved: 26 February 2024 / Online: 26 February 2024 (12:13:20 CET)
Sari, I.N.; Du, W. Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion. Appl. Sci.2024, 14, 2397.
Sari, I.N.; Du, W. Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion. Appl. Sci. 2024, 14, 2397.
Sari, I.N.; Du, W. Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion. Appl. Sci.2024, 14, 2397.
Sari, I.N.; Du, W. Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion. Appl. Sci. 2024, 14, 2397.
Abstract
Artistic image completion is fundamental for the preservation and restoration of invaluable art paintings and it has experienced significant progress through the implementation of deep learning methodologies. Despite these advancements, challenges persist, even with sophisticated approaches like Generative Adversarial Networks (GANs), particularly in achieving optimal results for high-resolution paintings. Small-scale texture synthesis and the inference of missing information from distant contexts present persistent issues, leading to distortions in lines and unnatural colors, especially in art paintings with complicated structures and textures. Concurrently, patch-based image synthesis has evolved by incorporating global optimization on the image pyramid to enhance structural coherence and details. However, methods relying on gradient-based synthesis encounter obstacles related to directionality, inconsistency, and the heavy computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a groundbreaking approach, integrating Weighted Similarity-Confidence Laplacian Synthesis, to comprehensively address these challenges and advance the field of artistic image completion. This proposal addresses challenges not only in high-resolution artistic image completion but also makes a significant contribution to the broader field of patch-based synthesis by utilizing the Laplacian pyramid for enhanced edge-aware correspondence search. Experimental result confirms the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with complicated details and irregular missing regions.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
