Research on the picture hierarchy enhancement technique based on probability distribution in oil paintings
Bibliographic record
Abstract
With the rapid development of computer vision technology, image enhancement technology involves an increasingly wide range of research content.At the current stage, picture hierarchy enhancement technology is a research hotspot in the field of image enhancement.This paper proposes an oil painting image enhancement network based on positive probability distribution guidance.The multidimensional spatial information of the samples is obtained through the multibranch information extraction architecture in the network structure, and the probability distribution estimation module estimates the probability distribution through the obtained multidimensional spatial information.In addition, a new image enhancement method based on the RGB color balance method is proposed, which combines the multi-scale Retinex enhancement algorithm with color recovery and the RGB, Lab color space histogram adaptive stretching algorithm, to further improve the effect of oil painting image display.The experimental results show that the method has a better image color bias correction effect compared with the existing techniques.In terms of subjective evaluation, the average subjective score of this paper's method in three different aesthetic levels reaches 9.15, obtaining a high evaluation.The samples enhanced based on this paper's algorithm all obtained high aesthetic index scores, indicating that the oil paintings under this paper's algorithm are in line with the public aesthetics, which is of great significance to the work of oil painting artists.
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 | 0.005 | 0.001 |
| 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 | 0.002 |
| 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".