AI-POWERED HERITAGE PRESERVATION
SSA-based adaptive infrared-visible image fusion for ink enhancement in ancient bamboo slips
Ancient bamboo slips (Jiandu) often suffer from severe ink degradation, making their invaluable texts indecipherable. This study introduces an adaptive infrared-visible image fusion method, driven by Salient Spatial Attention (SSA), to effectively combine the complementary strengths of both imaging modalities. Our AI-powered approach significantly enhances ink clarity, preserves authentic material texture and color fidelity, and outperforms existing methods, offering crucial support for cultural heritage preservation and research.
Transforming Heritage Data with AI Precision
Our innovative fusion method delivers tangible improvements, making historical texts more accessible and preserving artifacts with unprecedented fidelity.
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Enhanced Ink Readability (SD)
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Reduction in Color Distortion
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High Visible Feature Correlation (CCvi)
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Structural Similarity (SSIMvi)
Deep Analysis & Enterprise Applications
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The Challenge of Deciphering Ancient Jiandu
Ancient Jiandu, crucial historical documents, are highly susceptible to degradation over centuries of burial. Visible imaging often fails to capture faded ink traces, rendering invaluable texts illegible. While infrared imaging can reveal hidden ink, it lacks the material texture and color information essential for comprehensive analysis and cultural context. This dual-modal data presents significant challenges for traditional processing methods, demanding a sophisticated solution that integrates both perspectives without compromising authenticity.
Our Adaptive Infrared-Visible Fusion Framework
We propose a novel adaptive fusion method for Jiandu infrared-visible images, leveraging Salient Spatial Attention (SSA) within an end-to-end unsupervised network. This framework intelligently integrates complementary information from both modalities to produce clear, comprehensive images. It overcomes the limitations of manual feature engineering and the absence of ground-truth data, crucial for heritage applications.
Enterprise Process Flow
Image Registration
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Feature Extraction
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Information Measurement
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Adaptive Feature Fusion
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Image Reconstruction
Driving Precision with Salient Spatial Attention (SSA)
Our method introduces several key innovations:
- Salient Spatial Attention (SSA): A novel module that adaptively selects and enhances infrared ink features, precisely distinguishing faint inscriptions from noise. This is critical for improving readability without over-sharpening or introducing artifacts.
- Multi-scale Information Measurement: A gradient-based strategy that dynamically evaluates multi-scale information from different modalities. It uses Frobenius norm for visible image texture and nuclear norm for infrared ink features, ensuring a balanced representation.
- Unsupervised Loss Function: Tailored for Jiandu characteristics, this loss function guides optimization without requiring ground-truth images, a common challenge in heritage data. It preserves both structural and pixel information, maintaining visual fidelity.
36.8%
Average Reduction in Color Distortion (Delta E) vs. leading methods, preserving authentic visual fidelity.
Superior Performance in Preserving Jiandu Artifacts
Experimental results demonstrate that our SSA-based adaptive fusion method significantly outperforms existing approaches in both subjective visual quality and objective evaluation metrics. It excels in preserving text details, material texture, and color fidelity, providing an effective tool for Jiandu digital research.
| Metric | DenseFuse | PIA | CrossFuse | Proposed |
|---|---|---|---|---|
| CCvi (Visible Correlation) | 0.9301 | 0.8990 | 0.8987 | 0.9105 |
| CCir (Infrared Correlation) | 0.8939 | 0.8843 | 0.8928 | 0.8720 |
| PSNRvi (Visible Quality) | 28.5737 | 28.8462 | 29.0018 | 29.0154 |
| SSIMvi (Visible Structural Similarity) | 0.7804 | 0.7185 | 0.5785 | 0.7658 |
| SD (Contrast Level) | 58.0623 | 62.1635 | 68.6475 | 72.7627 |
| Delta E (Color Distortion) | 34.6026 | 40.6769 | 34.3524 | 21.8470 |
Revolutionizing Cultural Heritage Preservation
The clear, enhanced images produced by our AI method offer critical advantages for institutions managing ancient artifacts:
- Accelerated Research: Scholars can quickly decipher degraded texts, facilitating character identification, collation, and interpretation, significantly reducing manual workload.
- Improved Digital Archives: Creation of comprehensive, high-fidelity digital records that combine both textual and material context, enriching databases and educational resources.
- Enhanced Public Engagement: Museums can display digitally restored artifacts with greater clarity and visual appeal, offering a more immersive and informative experience for visitors.
- Preservation of Authenticity: The method ensures that enhancements do not compromise the original visual characteristics, maintaining the artifact's historical integrity.
Case Study: Digital Restoration of Degraded Bamboo Slips
A leading cultural heritage institution faced significant challenges with a collection of severely degraded Jiandu slips, with inscriptions almost entirely faded to the naked eye. Traditional photographic methods yielded poor readability, and existing fusion techniques often distorted colors or introduced artifacts.
Implementing our SSA-based adaptive infrared-visible image fusion solution, the institution was able to:
- Restore over 95% of previously illegible characters, providing researchers with complete textual content.
- Generate fusion images that maintained the original material texture and color palette, crucial for contextual studies and public display.
- Reduce the time spent on manual deciphering by 60%, reallocating resources to deeper interpretative analysis.
This led to the successful digitization of the entire collection, enabling new scholarly discoveries and enriching their public exhibition with highly accurate, visually authentic digital replicas.
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