Underwater Image Enhancement Through Smooth Gridded Adaptive Color Compensation with Green-Tint Removal and Integrated CLAHE
Bibliographic record
Abstract
Underwater images frequently have low contrast, color distortion, and a greenish hue, due to wavelength-dependent light absorption and scattering.These issues limit the efficacy of underwater imaging applications and negatively impact visual perception.This paper presents a unique enhancement framework called Smooth Gridded Adaptive Color Compensation (SGACC) with Green Tint Removal (GTR) and adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE) to address these issues.While GTR adjusts for red-channel attenuation to lessen green dominance, the SGACC module uses Gaussian-blended grids for smooth and targeted color correction.By adapting to changes in brightness while maintaining image features, adaptive CLAHE significantly improves local contrast.The proposed method consistently outperforms state-of-the-art techniques, according to experimental assessments on Large-Scale Underwater Image (LSUI) and Underwater Image Enhancement Benchmark (UIEB) datasets.It offers significant Underwater Image Quality Measure (UIQM) / Underwater Color Image Quality Evaluation (UCIQE) improvements, 2.0% Structural Similarity Index (SSIM) gain, and up to 3.6 dB better Peak Signal-to-Noise Ratio (PSNR) on LSUI.It achieves 21.11 dB PSNR and 0.9631 SSIM on UIEB, indicating exceptional perceptual quality.The proposed technique, SGACC-GTR, with adaptive CLAHE architecture, successfully restores natural color balance and enhances underwater image quality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".