Distributed wavefront coding for wide angle imaging system
Bibliographic record
Abstract
The emerging paradigm of imaging systems, known as wavefront coding, which employs joint optimization of both the optical system and the digital post-processing system, has not only increased the degrees of design freedom but also brought several significant system-level benefits. The effectiveness of wavefront coding has been demonstrated by several proof-of-concept systems in the reduction of focus-related aberrations and extension of depth of focus. While previous research on wavefront coding was mainly targeted at imaging systems having a small or modest field of view (FOV), we present a preliminary study on wavefront coding applied to panoramic optical systems. Unlike traditional wavefront coding systems, which only require the constancy of the modulation transfer function (MTF) over an extended focus range, wavefront-coded panoramic systems particularly emphasize the mitigation of significant off-axis aberrations such as field curvature, coma, and astigmatism. The restrictions of using a traditional generalized cubic polynomial pupil phase mask for wide angle systems are studied in this paper. It is shown that a traditional approach can be used when the variation of the off-axis aberrations remains modest. Consequently, we propose to study how a distributed wavefront coding approach, where two surfaces are used for encoding the wavefront, can be applied to wide angle lenses. A few cases designed using Zemax are presented and discussed
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".