Subwavelength imaging challenges in the infrared and THz wavebands
Bibliographic record
Abstract
Subwavelength imaging has recently seen increased interest in multiple fields. There are various applications and distinct contexts for performing subwavelength imaging. The technological ways to proceed as well as the benefits obtained are as various as the applications foreseen. To benefit from subwavelength imaging a way around standard imaging procedure is often required. INO is also involved in this activity mainly for the infrared and the THz wavebands. In the infrared band a detector with 17 um pixel pitch, larger than the pixel, was used in conjunction with a microscanning device to oversample the image at a pitch much smaller than the wavelength. In this case the pixel size is in the order of the wavelength but the sampling is at subwavelength level. In the THz band a 35 um pixel pitch is used at wavelength ranging from 70 um to 1,063 mm to perform imaging through various objects. In this case, the pixel itself is smaller than the wavelength. Subwavelength imaging is not without its challenges, though. For instance, while the use of ultra-fast optics provides better definition, their design becomes more challenging as the models used are at their very limits. Questions about information content of images can be raised as well. New research avenues are being investigated to help address the challenges of subwavelength imaging with the goal of achieving higher imaging system performance. This paper discusses aspects to be considered, review some results obtained and identify some of the key issues to be further addressed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".