Freeform wide-angle camera lens enabling mitigable distortion
Bibliographic record
Abstract
Allowing natural scenes as well as maximizing field of view (FoV) can benefit from the minimization of distortion for the wide-angle camera. The wide-angle camera utilizing freeform surfaces for mitigating distortions, either barrel distortion or pincushion distortion, is therefore of interest. In this paper, the designs of using all-aspherical surfaces and aspherical surfaces combined with freeform surfaces are investigated. To minimize the deviation before and after converting from aspherical surfaces to freeform surfaces, a mathematical conversion scheme is derived. By applying it to the design example, the methodology is shown to be effective in the case of an optical system with a large number of aspherical/freeform surfaces. Additionally, custom freeform analysis tools are developed for quantitative analysis and visualization of the critical characteristics of optical performance, namely, a 2D lateral color field map, 2D relative illumination field map, 2D spot radius field map, and 2D average modulation transfer function (MTF) field map. Compared to classical all-aspherical design, simulation results show that freeform design has the capability to reduce distortion, and other performances such as relative illumination, spot size, and MTF can also be improved, even though there are some compromises on the peripheral FoV. The design approach will have potential important research and application values for lens systems utilized in miniature camera lenses, especially the wide FoV capability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".