An Edge-based Image Interpolation Approach Using Symmetric Biorthogonal Wavelet Transform
Bibliographic record
Abstract
Edge-based image interpolation often leads to an image with good quality because of the importance of sharp edges and smooth contours to the human vision. The wavelet-based image interpolation approach has good potential in producing interpolated images with high quality edges. Using the wavelet multiresolution analysis theory, such method is computationally demanding. In this paper, a new edge-based image interpolation method that uses symmetric biorthogonal wavelet transform is proposed. We form a list of ideal step edges and study the relationships between the wavelet approximation sub-image of each edge and its wavelet detail sub-images. Based on these relationships, a simple and efficient algorithm that predicts the edge information of high resolution images is proposed. For this method, the 9/7-M inverse wavelet transform is shown, experimentally, to yield better image interpolation performance compared to traditional image interpolation approaches
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".