A joint dictionary-based method for single image super-resolution
Why this work is in the frame
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Bibliographic record
Abstract
Image super-resolution technique mainly aims at restoring high-resolution image with satisfactory novel details. In recent years, sparsity-based super-resolution has attracted great interests for its impressive results. By using learning dictionaries, sparsity-based methods try to find some mapping relationships as prior knowledge between low- and high-resolution example images for better reconstruction. In this paper, based on two of the state-of-the-art sparsity-based super-resolution methods, we propose a joint dictionary-based framework to improve the quality of reconstructed high-resolution images. Experimental results illustrate that our method outperforms the other state-of-the-art methods in terms of sharper edges, clearer textures and better novel details.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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