Using Wavefront Coding in presence of non-symmetric aberrations
Bibliographic record
Abstract
Wavefront coding is a hybrid technology designed to increase depth of field of conventional optics but it can also be used to compensate for other aberration and ease tolerancing. The goal of our research is to apply this technology to panoramic imager. Panoramic imagers suffer from an increase level of aberration due to the large field of view and it is also subject to a special tolerance process. They also typically have a wide variation of the point spread function (PSF) across the field of view and suffer from non-symmetric aberration like coma and astigmatism. To obtain the best result using wavefront coding, the PSF should be as invariant as possible over the whole field of view. Asymmetric phase masks, when used in systems having non-negligible asymmetric aberrations, generate variations in the final image quality. For that reason, a model that predicts the final image quality of wavefront coded system is needed. The possibility of using two surfaces for wavefront coding has been studied. The final results were analysed using a variance based image quality criterion. From these results, it is possible to optimize phase mask for panoramic imager and predict the resulting image quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".