A comprehensive dataset for digital restoration of Dunhuang murals
Bibliographic record
Abstract
The MuralDH dataset is an invaluable digital resource developed for the conservation and restoration of Dunhuang murals, which are critical components of global cultural heritage facing threats from degradation. This dataset comprises over 5000 high-resolution images tailored to 512 × 512 pixels, emphasizing the preservation of mural integrity and detail. It includes 1000 images with pixel-level damage annotations for segmentation research and 500 images specially processed for super-resolution studies, catering to a wide range of digital restoration needs. While the primary focus of this work is the dataset itself, we also introduce a supportive digital restoration framework. This framework, which encompasses damage segmentation, inpainting, and super-resolution techniques, serves as a secondary validation of MuralDH's utility and versatility. Through MuralDH, technology revives ancient art, embodying the essence of interdisciplinary innovation. By facilitating advanced research in computer vision and artificial intelligence, MuralDH aims to revolutionize the digital preservation practices for murals and other cultural artifacts, demonstrating the critical role of interdisciplinary collaboration in safeguarding our cultural legacy.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".