Video denoising in three-dimensional complex wavelet domain using a doubly stochastic modelling
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a new video denoising method in the three-dimensional (3D) discrete complex wavelet transform (DCWT) domain. The authors assume that the coefficients have zero mean and Gaussian local distributions given the unknown variances. In practice, the locally estimated variances (LEVs) are not accurate and are simply maximum-likelihood estimates from the conditional Gaussian distribution. To take into account the inaccuracies of LEVs and motivated by experiments, the authors assume that the LEVs have gamma distributions. This is equivalent to the unconditional heavy-tailed local Bessel K-form prior densities given LEVs. This model is able to more accurately model the intrascale dependency between adjacent wavelet coefficients. The authors employ both maximum a posteriori and minimum mean-squared error MMSE estimators of the unconditional distributions, to reduce the noise in the 3D DCWT domain. The authors examine their spatially adaptive algorithm for reduction of various types of noise including additive white Gaussian noise, non-stationary noise, Poisson noise and speckle noise. The proposed method results in an impressive video enhancement without any explicit use of motion estimation. This is because, the 3D DCWT is a motion selective transform and isolates the motions and directions in its sub-bands.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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 it