Computational Framework for Turbid Water Single-Pixel Imaging by Polynomial Regression and Feature Enhancement
Bibliographic record
Abstract
The quality of underwater imaging is greatly impacted by the scattering and absorption of light in turbid water environments. Single pixel imaging (SPI) has emerged as a promising solution for turbid underwater imaging, as it effectively suppresses the effects of scattering and is cost-effective due to the use of a single photodetector. However, the quality of SPI in highly turbid water is still unsatisfactory. To address this issue, we propose a novel computational framework for turbid water single-pixel imaging. The framework involves a machine learning-based polynomial regression fitting method, followed by data feature enhancement in the spectrum domain to obtain the rectified data, and ultimately, high-contrast image recovery. Furthermore, we propose a new metric, Edge Detection-based Enhancement Measure Evaluation (EDEME), to quantitatively evaluate the contrast of the recovered images. Our experimental results demonstrate that our proposed method can recover images in low turbidity water to a level comparable to clear water, and even in highly turbid water (turbidity greater than 50 NTU), the recovered images are legible with significantly improved EDEME values. Additionally, our method exhibits wide adaptability, requires minimal data operations, and outperforms some post-image processing methods. This work has significant implications for imaging, inspections, search and rescue, resource exploitation, and other applications in underwater environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".