Monochrome and color image denoising using neighboring dependency and data correlation
Bibliographic record
Abstract
In this paper, two approaches for image denoising that take advantages of neighboring dependency in the wavelet domain are studied. The first approach is to take into account the higher order statistical coupling between neighboring wavelet coefficients and their corresponding coefficients in the parent level. The second is based on multivariate statistical modeling. The estimation of the clean coefficients is obtained by a general rule using Bayesian approach. Various estimation expressions can be obtained by <i>a priori</i> probability distribution, called multivariate generalized Gaussian distribution (MGGD). The experimental results show that both of our methods give comparatively higher peak signal to noise ratio (PSNR) as well as little visual artifact for monochrome images. In addition, we extend our approaches to a denoising algorithm for color image that has multiple color components. The proposed color denoising algorithm is a framework to consider the correlations between color components yet using the existing monochrome denoising method without modification. Denoising results in this framework give noticeable better improvement than in the case when the correlation between color components is not considered.
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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 | 0.001 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".