Image Inpainting using Automatic Structure Propagation with Auxiliary Line Construction

Urano, Yuto; Sari, Irawati Nurmala; Du, Weiwei

doi:10.1109/snpd-summer57817.2022.00026

“…Researchers have navigated challenges common to both, necessitating robust generalization capabilities for effective image completion. A spectrum of techniques has been explored, from deep neural networks [4,[6][7][8][9][10][11][12] to traditional methods [13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

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

Sari,

Du

2024

Preprint

0

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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.

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“…Researchers have navigated challenges common to both, necessitating robust generalization capabilities for effective image completion. A spectrum of techniques has been explored, from deep neural networks [4,[6][7][8][9][10][11][12] to traditional methods [13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

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

Sari,

Du

2024

Applied Sciences

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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.

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“…Researchers have navigated challenges common to both, necessitating robust generalization capabilities for effective image completion. A spectrum of techniques has been explored, from deep neural networks [4,[6][7][8][9][10][11][12] to traditional methods [13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

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

Sari,

Du

2024

Preprint

0

View full text Add to dashboard Cite

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.

show abstract

Image Inpainting using Automatic Structure Propagation with Auxiliary Line Construction

Cited by 5 publications

References 23 publications

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

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

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

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

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