Metalens-Based Compressed Ultracompact Femtophotography: Analytical Modeling and Simulations
Bibliographic record
Abstract
Single-shot 2-dimensional optical imaging of transient phenomena is indispensable for numerous areas of study. Among existing techniques, compressed ultrafast photography (CUP) using a chirped ultrashort pulse as active illumination can acquire nonrepetitive time-evolving events at hundreds of trillions of frames per second. However, the bulky size and conventional configurations limit its reliability and application scopes. Superdispersive metalenses offer a promising solution for an ultracompact design with a stable performance by integrating the functions of a focusing lens and dispersive optical components into a single device. Nevertheless, existing metalens designs, typically optimized for the full visible spectrum with a relatively low spectral resolution, cannot be readily applied to active-illumination CUP. To address these limitations, here, we propose single-shot compressed ultracompact femtophotography (CUF) that synergically combines the fields of nanophotonics, optical imaging, compressed sensing, and deep learning. We develop the theory of CUF’s data acquisition composed of temporal–spectral mapping, spatial encoding, temporal shearing, and spatiotemporal integration. We also develop CUF’s image reconstruction via deep learning. Moreover, we design and evaluate CUF’s crucial components—a static binary transmissive mask, a superdispersive metalens, and a 2-dimensional sensor. Finally, using numerical simulations, CUF’s feasibility is verified using 2 synthetic scenes: an ultrafast beam sweeping across a surface and the propagation of a terahertz Cherenkov wave.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".