An Optical Imaging Technique Using Deep Illumination in the Angular Domain
Bibliographic record
Abstract
This paper describes a novel optical imaging method, deep illumination angular domain imaging (ADI), for detecting micron-scale objects within highly scattering media. The new optical imaging is a much simpler and less expensive solution as compared to other available optical imaging techniques. In principle, deep illumination ADI uses collimation detection capabilities of small acceptance angle devices to extract photons emitted from the scattered light created by a laser source, aimed deep beneath the turbid medium surface. The laser source forms an illumination ball within the medium that emits scattered light in all directions and illuminates objects near the surface from behind. Consequently, when photons from this illumination ball pass an object and reach the angular filter, light that is not subsequently scattered passes through to a camera detector, whereas scattered photons are rejected by the filter. Image results obtained are recorded for different phantom locations, phantom sizes, and medium scattering levels. Our images clearly display sub-204 m phantoms when placed 3 mm deep within a test scattering medium with total effective attenuation coefficient (mu'eff) up to 5.8-1cm or 2.5 mm deep in chicken tissue tests. Preliminary digital image processing shows the image contrast enhancement and the definition improvement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".