Image Restoration under Cauchy Noise: A Group Sparse Representation and Multidirectional Total Generalized Variation Approach
Bibliographic record
Abstract
Owing to the ill-posed problem of image restoration, how to find an effective method to obtain image prior information is still challenging.The total generalized variational model has been successfully applied to image denoising and/or deblurring.However, the highorder gradient of the image is described by using L1 norm in the traditional total generalized variational denoising and deblurring model, which can not effectively describe the local group sparse priors of the image gradient.As a result, the traditional total generalized variational model has some limitations in the ability to suppress the staircase artifacts.In order to solve this problem, one new model is proposed to restore images corrupted by Cauchy noise and/or blur in our paper, where the non-convex data fidelity term is combined with two regularization terms: group sparse representation prior and multi-directional total generalized variation.We use group sparse representation prior information to obtain the nonlocal self-similarity information of similar image block for preserving the details and texture features of the discontinuous or uneven region of the image.At the same time, the noise is fully removed in the uniform region, which improves the image visual quality.Moreover, the gradient information in multiple directions is calculated by the multidirectional total generalized variation regularization term, which can better preserve the edge information of the image.The model is divided into several sub-problems by split Bregman iteration, and each sub-problem is solved efficiently.The experimental results show that this model is superior to other existing models both in terms of visual quality and some image quality evaluation.
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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 | 0.000 | 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".