Enhanced angular domain optical imaging by background scattered light subtraction from a deviated laser source
Bibliographic record
Abstract
Imaging structures within a turbid medium using Angular Domain Imaging (ADI) employs angular filter array aligned to a laser source to separate ballistic and quasi-ballistic photons from the highly scattered light by means of angular filtration. The angular filter consists of a high aspect ratio linear array of silicon micromachined tunnels, 51 micron wide by 10mm long with a 0.29 degree acceptance angle. At heavy scattering ratios of >1E7 image detectability declines due to the non-uniform scattered background light fraction still within the acceptance angle. This scattered signal can be separated out by introducing a wedge prism to deviate the laser source where it enters the medium by an angle slightly larger than the acceptance angle. This creates a second image consisting of pure scattering photons with the filtration characteristics of the angular filter, and a pixel by pixel correspondence to the fully scattered illumination emitted from the medium. Experiments used an 808 nm laser diode, collimated to an 8×1 mm line of light, entering a 5cm thick medium with a scattering ratio of > 1E6, with a wedge prism creating a 0.44 degree deviation. Digitally subtracting the deviated scattered signal from the original image significantly reduced the scattered background and enhanced image contrast. We can have about images at least 40 times more of our previous scattering limits. Depending on test phantom object location, the contrast level can be increased from 4% of the total dynamic range to over 50% which results in higher definition and visibility of our micro-scale test structures in the turbid medium.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".