Hyperspectral imaging: comparison of acousto-optic and liquid crystal tunable filters
Bibliographic record
Abstract
In this work, we report a performance comparison of an acousto-optic tunable filter (AOTF), and a liquid crystal tunable filter (LCTF) based on a novel dual-arm hyperspectral imaging (HSI) configuration. The main purpose of this work is to highlight the leverage points of each tunable filter, in order to facilitate filter choice in HSI design. Three main parameters are experimentally examined: spectral resolution, out-of-band suppression, and image quality in the sense of spatial resolution. The experimental results, using wideband illumination, laser lines, and a spatial test target (USAF-1951) emphasized the superiority of AOTF in spectral resolution, out-of –band suppression and random switching speed between wavelengths. The same experiments demonstrated LCTF to have better performance in terms of the spatial image resolution, both horizontal and vertical, and high definition quality. In conclusion, the efficient design of an HSI system is application-dependent. For medical applications, for instance, if the tissue of interest has undefined optical properties, or contains close spectral features, AOTF might be the better option. Otherwise, LCTF is more convenient and simpler to use, especially if the tissue chromophore’s spatial mapping is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".