Digital Protection and Recovery Method of Traditional Paper Horse Art Based on Image Processing Technology
Bibliographic record
Abstract
The argument of the article comes from the rapid development of digital technology and the urgent need for the digital protection and restoration of traditional paper horse art.For this reason, this paper proposes a method of digital protection and restoration of traditional paper horse art based on graphics processing technology.The traditional paper horse art image is collected, the image is denoised using mean filtering, the paper horse image is decomposed in gray scale through spatial conversion, and then its double histogram equalization is processed to obtain the color-enhanced image.Combined with the convolutional image restoration strategy, the paper horse art is digitally displayed.The method of this paper can enhance the color of the paper horse art image and retain the original details, and at the same time, in terms of the clarity effect, the method of this paper improves the comparison method by 25.27%~339.39%.In addition, the method in this paper has better image restoration quality with subjective evaluation rating ≥ 4 and higher PSNR and SSIM.What's more, the scores on the evaluation dimension of digital preservation and restoration effect ranged from 4.02 to 4.48, and the overall effect performance was relatively good.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".