A new bivariate MAP estimator for DT-CWT-based video denoising
Bibliographic record
Abstract
A new bivariate maximum a posteriori estimator is proposed for the magnitude components of the dual-tree complex wavelet transform (DT-CWT) coefficients in order to reduce additive white Gaussian noise in a video. The estimator considers the fact that the magnitude components of the DT-CWT coefficients of the Gaussian distributed noise fit the generalized Gamma distribution very well. For spatial filtering, the joint distribution function of the magnitude components of the DT-CWT coefficients of the two neighboring frames of a video is considered to be locally-adaptive bivariate Gaussian having a non-negative mean. The correlation coefficient of this distribution function acts as an indirect measure of the motion of the DT-CWT coefficients between two neighboring frames. A recursive time averaging of the spatially filtered magnitude components is adopted for further noise reduction. Experimental results on test video sequences show that the proposed estimator provides an average peak signal-to-noise ratio that is higher than that provided by the others.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".