Exploiting k-space/frequency duality in Fourier optics toward real-time compression less terahertz imaging
Bibliographic record
Abstract
We present a theoretical formulation and an experimental demonstration of a fast compression-less terahertz imaging technique based on broadband Fourier optics. The technique exploits k-vector/frequency duality in Fourier optics which allows to use a single-pixel detector to perform angular scan along a circular path, while the broadband spectrum is used to scan along the radial dimension in Fourier domain. The proposed compression-less image reconstruction technique (hybrid inverse transform) requires only a small number of measurements that scales linearly with the image linear size, thus promising real-time acquisition of high-resolution THz images. We develop an algorithm based on a polar formulation of the Fourier transform to reconstruct the image. First, we show how the equations are transformed when passing from a spatial integral to a frequency integral. Second, we analytically demonstrate that, in the case of binary amplitude objects and phase objects, the reconstructed image from our formulation is proportional to the original object. Third, we experimentally demonstrate the image reconstruction method in the two above-mentioned cases: we use a metal aperture for the binary object and an engraving in a polymer sample for the phase object. A detailed analysis of the novel technique advantages and limitations is presented, and its place among other existing THz imaging techniques is clearly identified.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".