Using Wavefront Coding in presence of non-symmetric aberrations
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it