Focus Measure for Synthetic Aperture Imaging Using a Deep Convolutional Network
Bibliographic record
Abstract
Synthetic aperture imaging is a technique that mimics a camera with a large virtual convex lens with a camera array. Objects on the focal plane will be sharp and off the focal plane blurry in the synthesized image, which is the most important effect that can be achieved with synthetic aperture imaging. The property of focusing makes synthetic aperture imaging an ideal tool to handle the occlusion problem. Unfortunately, to automatically measure the focusness of a single synthetic aperture image is still a challenging problem and commonly employed pixel-based methods include using variance or using a ”manual focus” interface. In this paper, a novel method is proposed to automatically determine whether or not a synthetic aperture image is in focus. Unlike conventional focus estimation methods which pick the focal plane with the minimum variance computed by the variance of corresponding pixels captured by different views in a camera array, our method automatically determines if the synthetic aperture image is focused or not from one single image of a scene without other views using a deep neural network. In particular, our method can be applied to automatically select the focal plane for synthetic aperture images. The experimental results show that the proposed method outperforms the traditional automatic focusing methods in synthetic aperture imaging as well as other focus estimation methods. In addition, our method is more than five times faster than the state-of-the-art methods. By combining with object detection or tracking algorithms, our proposed method can also be used to automatically select the focal plane that keeps the moving objects in focus. To the authors’ best knowledge, it is the first time that such a method of using a deep neural network has been proposed for estimating whether or not a single synthetic aperture image is in focus.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".