Motion Adaptive Video Denoising in the Wavelet Domain Based on Bivariate Shrinkage
Bibliographic record
Abstract
This paper proposes a new wavelet-based video noise reduction algorithm. Each frame in the video sequence is transformed to the wavelet domain using the dual-tree complex wavelet transform. We model the interscale dependencies in subband representation of each frame using a non-Gaussian bivariate distribution. The parameters for this bivariate distribution are estimated adaptively using the local correlations that exist between neighboring coefficients within each subband. Based on this bivariate distribution a shrinkage function is developed using the maximum a posteriori (MAP) rule. To improve the performance of the filter, information from the adjacent frames is also incorporated in the shrinkage function. This is achieved by detecting the motion between the corresponding subband coefficients in successive frames. Experimental results show that the proposed scheme outperforms several state-of-the-art spatio-temporal filters in terms of the peak signal to noise ratio and the visual quality.
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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 | 0.005 | 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 | 0.000 |
| 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 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".