Underwater image enhancement with latent consistency learning‐based color transfer
Bibliographic record
Abstract
Abstract Due to the inevitable wavelength‐dependent light absorption and forward/backward scattering, underwater images usually suffer severe color distortion and are hazy. It has become quite necessary to improve the visual quality of underwater images for both underwater observation and operation. Traditional enhancement methods and existing deep learning‐based approaches to underwater image enhancement usually produce unsatisfactory results for photographs taken in complicated, wild underwater scenes. In such scenes, complex and diverse degradation‐enhancement mappings are often difficult to model, especially since there are very limited samples available for learning. Inspired by the success of color‐transfer techniques, it is found that clear template image‐assisted color transfer is a promising strategy for underwater image enhancement, including not only color correction but also contrast and visibility improvement. Therefore, instead of directly learning the complex deep enhancement models, it is proposed to select proper color‐transfer templates by learning the latent consistency between the templates and the raw underwater images. The proposed new enhancement strategy alleviates the problem caused by incomplete color‐correction models and provides more stable enhancements by utilizing color transfer with consideration of global color distribution consistency and local visual contrast. Comprehensive experiments conducted on UIEB, RUIE, URPC and SQUID datasets demonstrate the good performance and great potential of the proposed new underwater image enhancement strategy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".