Tunable Mid‐Infrared Detail‐Enhanced Imaging With Micron‐Level Spatial Resolution and Photon‐Number Resolving Sensitivity
Bibliographic record
Abstract
Abstract The underdevelopment of mid‐infrared (MIR) components and detectors greatly limits the spatial resolution and sensitivity of MIR imaging. To overcome these limitations, MIR detail‐enhanced imaging is enhanced via non‐degenerate optical parametric amplification (OPA) pumped by a femtosecond vortex pulse. This design renders MIR illumination into a visible image by nonlinear wavelength‐conversion, together with a high OPA gain, large spatial bandwidth, and remarkable sensitivity. These experiments show that the design can realize MIR imaging with a spatial resolution of up to 114 line pairs per millimeter and a 2D spatial bandwidth product of up to 62 900, over a spectral region tunable from 2.0 to 3.0 µm. Equally important, this setup simultaneously achieves excellent imaging sensitivity of 25 photons at room temperature. It is thought that this work provides a powerful way to realize effective real‐time MIR imaging with an excellent spatial resolution even in very weak illumination environments, which can benefit many applications from semiconductor material characterization and biomedical imaging to security.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".