Virtual cleaning of sooty mural hyperspectral images using the LIME model and improved dark channel prior
Bibliographic record
Abstract
Murals, as important carriers of cultural heritage and historical records, showcase artistic, aesthetic, social, and political significance. In ancient times, religious activities such as burning incense and candles in temples led to many murals being polluted by soot, causing them to darken, lose details, and, in severe cases, completely blacken. As a result, the development of efficient virtual cleaning methods has become a key strategy for addressing this issue. In this study, we use synthetic true colour and false colour images in different bands of the hyperspectral spectrum, and use a guided filter fusion technique to fuse these two images into a new image of the sooty mural. Through analyzing the histograms and colour distribution scatterplots of the synthetic sooty mural images, we observed significant similarities to low-luminance images. To enhance the synthesized murals, we applied the LIME model. In addition, comparisons of the histograms and colour distribution scatterplots of the enhanced sooty mural images with those of haze images revealed notable similarities. Therefore, we applied the dark channel prior algorithm to remove soot from the mural images. Considering that soot particles are larger than haze particles, we introduced guided filtering to refine the transmission map and created a nonlinear transformation function to enhance its details. In terms of both visual perception and quantitative analysis, the proposed method significantly outperforms previous methods in the virtual cleaning of sooty murals. This technology can not only restore the colours and details of murals but also provide new clues for subsequent mural studies, allowing people to once again appreciate the true beauty of the murals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.001 |
| 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 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".