Distortion controlled optical design using orthogonal surface polynomials
Bibliographic record
Abstract
The design of high quality very wide-angle optical systems (FFOV < 100°) relies on distortion to obtain a sufficient resolution at, usually, the center of the field of view. Using distortion shape as a parameter during optimization to reach a magnification target is a common technique to achieve optical foveation during the lens design process. This method allows resolution enhancement at selected parts of the field of view since less care is given to parts of the image that are deemed less important. However, accurate control of distortion can be a challenge during optical design since the standard aspherics polynomials don’t correlate directly to image magnification. This may in fact slow down optimization since the merit function is much less optimized to approach a solution. In this paper, we address this problem by presenting a method to simplify distortion control during the optical design phase. To achieve this, the use of orthogonal polynomials is used for defining the optical surface shape and will then be used to compare the height of the image plane at a given field of view. We show that in the case of simple and paraxial system, this process is orthogonal and achieve a solution in a single optimization step. We will finally discuss the limits of this method and how it applies to modern lens design problems.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".