A hybrid enhancement algorithm for polarised images based on a dark primary color prior
Bibliographic record
Abstract
As the further development of marine resources, the demand for underwater target detection has also increased. Conventional sonar detection cannot meet the high-precision visible light detection requirements. There are abundant impurity particles in natural water bodies, and traditional visible light detection techniques are seriously affected by scattering, resulting in short detection distances and low image quality. This paper utilizes the high anti-interference capability of polarized light and employs a polarized light detection imaging system to detect turbid underwater targets. To address the issues of large dark areas, low contrast, and color distortion in underwater images, a hybrid enhancement algorithm based on a dark primary color prior is proposed. The factors affecting polarized imaging in water are analyzed, and an image quality evaluation system is established. An improved median filter with average polarization is introduced, and the dark primary color prior algorithm is improved by introducing a compensation value δ and a quantitative parameter k. The experimental images are evaluated using EME and NIQE, and the results show that the EME value of the processed images is increased by about 4 times, and the NIQE value is decreased by nearly 35%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".