Sparse Representation Prior and Total Variation--Based Image Deblurring under Impulse Noise
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Bibliographic record
Abstract
In this paper, we study the image recovery problem where the observed image is simultaneously corrupted by blur and impulse noise. Our proposed patch-based model contains three terms: the sparse representation prior, the total variation regularization, and the data-fidelity term. We are interested in the two-phase approach. The first phase is to identify the possible impulse noise positions; the second phase is to recover the image via the patch-based model using noise position information. An alternating minimization method is then applied to solve the model. This approach works extremely well for image deblurring under salt-and-pepper noise. However, as the detection for random-valued noise is usually unreliable, extra work is then needed. Indeed, to get better recovery results for the latter case, we combine the two separate phases to simultaneously detect the random-valued noise positions and to recover the image. The numerical experiments clearly demonstrate the super performance of the proposed methods.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| 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