What does an aberrated photo tell us about the lens and the scene?
Bibliographic record
Abstract
We investigate the feasibility of recovering lens properties, scene appearance and depth from a single photo containing optical aberrations and defocus blur. Starting from the ray intersection function of a rotationally-symmetric compound lens and the theory of Seidel aberrations, we obtain three basic results. First, we derive a model for the lens PSF that (1) accounts for defocus and primary Seidel aberrations and (2) describes how light rays are bent by the lens. Second, we show that the problem of inferring depth and aberration coefficients from the blur kernel of just one pixel has three degrees of freedom in general. As such it cannot be solved unambiguously. Third, we show that these degrees of freedom can be eliminated by inferring scaled aberration coefficients and depth from the blur kernel at multiple pixels in a single photo (at least three). These theoretical results suggest that single-photo aberration estimation and depth recovery may indeed be possible, given the recent progress on blur kernel estimation and blind deconvolution.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".