Far-Field Imaging Beyond the Diffraction Limit Using Waves Interference
Bibliographic record
Abstract
Due to the wave nature of light, resolution of optical imaging systems is limited to approximately half of the wavelength. The reason behind this limitation, known as diffraction limit, is the loss of information contained in evanescent waves at the far-field region. Here, we propose a new method to retrieve the information contained in evanescent waves in far field region resulting in a novel sub-wavelength imaging technique which can go beyond the diffraction limit. We theoretically prove that using interference of waves, between the target field and reference and reconstruction waves, one can apply a shift to the angular spectrum of the target field and convert a range of evanescent waves into propagating modes. Moreover, we demonstrate how these converted waves can be distinguished in far-field from other existing modes. Unlike previously developed sub-wavelength imaging techniques, the proposed method does not require either fluorescent materials or complex nano-structures to realize evanescent-to-propagating wave conversion. The performance of the method is numerically investigated illustrating a resolution of one-seventh of the working wavelength, which is much beyond the diffraction limit. The proposed technique can significantly simplify sub-wavelength imaging paving the road to develop practical low cost super-resolution imaging systems.
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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 | 0.001 |
| 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".