Bibliographic record
Abstract
Different models for estimating depth from defocused images have been proposed over the years. Typically two differently defocused images are used by these models. Many of them work on the principle of transforming one or both of the images so that the transformed images become equivalent. One of the most common models is to estimate the relative blur between a pair of defocused images and compute depth from it. Another model known as the Blur Equalization Technique (BET) works by blurring both images by an appropriate pair of blur kernels. The inverse approach is to deblur both images by an appropriate pair of blur kernels. In this paper we compare the performance of these models to find under what conditions they work best. We show that the common approach of using the Gaussian approximation of the relative blur kernel performs worse than a more general approximation of the relative blur kernel. Furthermore, we show that despite the reduction in signal content in BET, it works well in most circumstances. Finally, the performance of deconvolution based approaches depends on a large part on the shape of the blur kernel and is more appropriate for the coded aperture setup.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".