Wide-angle lens miniaturization through foveated imaging
Bibliographic record
Abstract
In the recent years, there have been many improvements in optics miniaturization, including wide-angle lenses. However, the design of a miniature wide-angle lens (FFOV 180°) is not a simple task. In order to correct aberrations that are issue from the large field of view, many lenses are necessary. Moreover, the amount of distortion is usually very high for those kinds of designs. It has been reported that distortion can be used as a design parameter in order to control the local magnification of the image across the field of view. This control of the distortion can be used to enhance the quality of the information present at the center of the image at the expense of the sides, leading to a foveated design. By carefully adjusting the resolution across the field of view, less care can be given to correcting defects issue from the edge of the field. This sort of compromise is a promising way to release some constraints and could, for example, allow a reduction of the number of lenses in the system. The present paper explores the effect of the control of distortion toward foveated imaging on a wide-angle lens. The goal is to assess its potential for allowing the simplification of the system. In order to achieve this objective, a miniature wide-angle lens is modified into different foveated designs, each of them with different resolution targets. The starting design is a state of the art commercial miniature wide-angle. The conditions in which the system can be reduced are then analyzed. Finally, the results and findings are 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".