An extensive empirical evaluation of focus measures for digital photography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatic focusing of a digital camera in live preview mode, where the camera’s display screen is used as a viewfinder, is done through contrast detection. In focusing using contrast detection, a focus measure is used to map an image to a value that represents the degree of focus of the image. Many focus measures have been proposed and evaluated in the literature. However, previous studies on focus measures have either used a small number of benchmarks images in their evaluation, been directed at microscopy and not digital cameras, or have been based on <i>ad hoc </i>evaluation criteria. In this paper, we perform an extensive empirical evaluation of focus measures for digital photography and advocate using three standard statistical measures of performance— precision, recall, and mean absolute error—as evaluation criteria. Our experimental results indicate that (i) some popular focus measures perform poorly when applied to autofocusing in digital photography, and (ii) simple focus measures based on taking the first derivative of an image perform exceedingly well in digital photography.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it