MuralDiff: Diffusion for Ancient Murals Restoration on Large-Scale Pre-Training
Why this work is in the frame
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Bibliographic record
Abstract
This paper focuses on the crack detection and digital restoration of ancient mural cultural heritage, proposing a comprehensive method that combines the Unet network structure and diffusion model. Firstly, the Unet network structure is used for efficient crack detection in murals by constructing an ancient mural image dataset for training and validation, achieving accurate identification of mural cracks. Next, an edge-guided optimized masking strategy is adopted for mural restoration, effectively preserving the information of the murals and reducing the damage to the original murals during the restoration process. Lastly, a diffusion model is employed for digital restoration of murals, improving the restoration performance by adjusting parameters to achieve natural repair of mural cracks. Experimental results show that comprehensive method based on the Unet network and diffusion model has significant advantages in the tasks of crack detection and digital restoration of murals, providing a novel and effective approach for the protection and restoration of ancient murals. In addition, this research has significant implications for the technological development in the field of mural restoration and cultural heritage preservation, contributing to the advancement and technological innovation in related fields.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it