Introducing sub-wavelength pixel THz camera for the understanding of close pixel-to-wavelength imaging challenges
Bibliographic record
Abstract
Conventional guidelines and approximations useful in macro-scale system design can become invalidated when applied to the smaller scales. An illustration of this is when camera pixel size becomes smaller than the diffraction-limited resolution of the incident light. It is sometimes believed that there is no benefit in having a pixel width smaller than the resolving limit defined by the Raleigh criterion, 1.22 λ F/#. Though this rarely occurs in today's imaging technology, terahertz (THz) imaging is one emerging area where the pixel dimensions can be made smaller than the imaging wavelength. With terahertz camera technology, we are able to achieve sub-wavelength pixel sampling pitch, and therefore capable of directly measuring if there are image quality benefits to be derived from sub-wavelength sampling. Interest in terahertz imaging is high due to potential uses in security applications because of the greater penetration depth of terahertz radiation compared to the infrared and the visible. This paper discusses the modification by INO of its infrared MEMS microbolometer detector technology toward a THz imaging platform yielding a sub-wavelength pixel THz camera. Images obtained with this camera are reviewed in this paper. Measurements were also obtained using microscanning to increase sampling resolution. Parameters such as imaging resolution and sampling are addressed. A comparison is also made with results obtained with an 8-12 μm band camera having a pixel pitch close to the diffractionlimit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".