Efficient box-constrained “nonconvex + nonconvex” approach for image deblurring with impulse noise
Bibliographic record
Abstract
To overcome the biases in estimating the L 1 -norm data fidelity term and staircase artifacts of the total variation regularization term, we propose a nonconvex+nonconvex model with box constraints to recover images degraded by blurring and impulse noise.Owing to the data fidelity term and the regularization term being nonconvex, we apply a proximal linearized minimization algorithm to solve the problem.To deal with a subproblem, we utilize the alternating direction multiplier method.The global convergence of the proposed algorithm is established under the assumption that the objective function satisfies the Kurdyka-Lojasiewicz property.We also present numerical results to demonstrate that the proposed nonconvex+nonconvex model outperforms existing models in terms of both numerical accuracy and visual quality.The proposed model also exhibits much better performance than the other methods, especially for piecewise-constant images.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".