Improved image selection for focus stacking in 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
Focus stacking, or all-in-focus imaging, is a technique for achieving larger depth of field in an image by fusing images acquired at different focusing distances. Minimizing the set of images to fuse, while ensuring that the resulting fused image is all-in-focus, is important in order to avoid long image acquisition and post-processing times. Recently, an end-to-end system for focus stacking has been proposed that automatically selects images to acquire. The system is adaptive to the scene being imaged and shows excellent performance on a mobile device, where the lens has a short focal length and fixed aperture, and few images need to be selected. However, with longer focal lengths, variable apertures, and more selected images (as exists with other cameras, notably DSLRs), classification and algorithmic inaccuracies become apparent. In this paper, we propose improvements to previous work that remove these limitations, and show on eight real scenes that overall our techniques lead to improved accuracy while reducing the number of required images.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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