Image Magnification Based on Bicubic Approximation with Edge as Constraint
Why this work is in the frame
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Bibliographic record
Abstract
Image magnification can be reduced to construct an approximation surface with data points in the image while keeping image details and edge features. In this paper, a new image magnification method is proposed by constructing piecewise bicubic polynomial surfaces constrained by edge features. The main innovation includes three parts. First, on the small adjacent area of each pixel, the new method constructs a quadratic polynomial sampling patch to approximate pixels on the small neighborhood with edge features as constraints. All quadric polynomial sampling patches are weighted to generate piecewise whole bicubic polynomial sampling surface. Second, a technique for calculating the error image is proposed: the error image is used to construct a correction surface to improve the accuracy and visual effect of the magnified image. Finally, in order to improve the accuracy of the approximation surface, a technology of balancing polynomial coefficients is put forward. Experimental results show that, compared with other methods, the proposed method makes better use of the local feature information of the image, which not only improves the PSNR/SSIM numerical accuracy of the magnified image but also improves the visual effect of the magnified 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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