All-In-Focus Synthetic Aperture Imaging Using Image Matting
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Bibliographic record
Abstract
“Seeing through” occluders is one of the most important effects that can be achieved with synthetic aperture imaging. As well, the occlusion problem, a challenging task for many computer vision applications, can be easily handled. Synthetic aperture imaging takes advantage of the property that only objects on the focal plane are sharp. The resulting image that is obtained by averaging images from different views consists of blurry objects away from the focal plane and sharp objects on the focal plane. Removing the blurriness caused by defocusing in synthetic aperture images to achieve an all-in-focus “seeing through” image is a challenging research problem. In this paper, we propose a novel method to improve the image quality of synthetic aperture imaging using image matting via energy minimization by estimating the foreground and the background. In particular, we first estimate the out-of-focus region by focusing on the background objects in each camera view using energy minimization. Next, we utilize a labeling method to create a sharp “see through” synthetic aperture image of the hidden objects. Then, image matting is used to extract the alpha matte of the hidden objects. Finally, by compositing the hidden objects with the estimated background regions, a sharp “see through” synthetic aperture image is created. The experimental results show that the proposed method outperforms the traditional synthetic aperture imaging method [1] as well as its improved versions [2]-[4], which simply dim and blur the area in the image that is out of focus, and a recent all-in-focus method [5]. We show that both the occluded objects and the background can be combined using our method to create a sharp synthetic aperture image.
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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 | 0.001 | 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