Depth from defocus using angle sensitive pixels based on a Transmissive Diffraction Mask
Bibliographic record
Abstract
An object in the scene whose image appears to be in focus is said to lie within the depth of field. Objects outside this volume appear blurred in the image. The shape of this defocus blur depends on the aperture. For a symmetric aperture, defocus blur has the same shape on either side of the focus plane. A consequence of this is that Depth from Defocus (DFD) methods that estimate object depth from defocus blur run into a blur ambiguity problem with symmetric apertures. While some methods use multiple images to resolve this ambiguity, it is a computationally heavy process. Airy3D, a start-up based in Montréal, Québec proposed an image sensor with Angle Sensitive Pixels (ASPs) based on a Transmissive Diffractio nMask (TDM). An ASP is a pixel that can detect light's intensity as well as its angle of incidence. ASPs have many advantages over regular pixels that respond only to the intensity of incident light. One such advantage lies in resolving the blur ambiguity. This thesis proposes a single image DFD approach which is free from the blur width ambiguity problem. This proposed DFD method using information about the angle of incidence of light provided by ASPs provide unambiguous blur width estimates even for symmetric apertures.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".