Research of application of high-repetition-rate green laser in underwater imaging system
Bibliographic record
Abstract
It is commonly known that absorption and scattering are the main causes of reducing performance of imaging system, especially imaging distance and resolution. Generally, various techniques are applied to decrease the effect of scattering, such as synchronous scanning and range-gated technique. Continuous-laser imaging technique meets requirements of imaging objects in the large field of view in real time, but imaging distance is less than 2 attenuation lengths in natural water. High-repetition-rate green laser, called quasi-continuous wave (QCW) green laser, is a better light source for underwater imaging. It has 1 kHz-100 kHz modulated rate, and its single pulse peak power is KW magnitude, which can be applied to range-gated imaging as Canadian LUCIE system. In addition, its polarization property is excellent for underwater polarization imaging. Therefore, it has enormous potential to underwater imaging. In order to realize its performance in underwater imaging system, we setup a separated underwater staring imaging system. For this system, a theoretic model is built by the lidar equation and optic transmission theory, and the system is evaluated by modulation transfer function (MTF). The effects of laser and receiver’s parameters for the system are analyzed. Then the comparative experiments are conducted in turbid water in laboratory. The results indicate that high pulse energy improves imaging distance. Aperture and polarization could reduce the effect of scattering effectively in staring system. The result shows that this underwater system performs better by choosing suitable parameters of source and receiver.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".