A nonlocal-means approach to exemplar-based inpainting
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a novel approach to the problem of image inpainting through the use of nonlocal-means. In traditional inpainting techniques, only local information around the target regions are used to fill in the missing information, which is insufficient in many cases. More recent inpainting techniques based on the concept of exemplar-based synthesis utilize nonlocal information but in a very limited way. In the proposed algorithm, we use nonlocal image information from multiple samples within the image. The contribution of each sample to the reconstruction of a target pixel is determined using an weighted similarity function and aggregated to form the missing information. Experimental results show that the proposed method yields quantitative and qualitative improvements compared to the current exemplar-based approach. The proposed approach can also be integrated into existing exemplar-based inpainting techniques to provide improved visual quality.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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