Low-complexity video noise reduction in wavelet domain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a novel spatio-temporal filter for video denoising that operates entirely in the wavelet domain and is based on temporal decorrelation. For effective noise reduction, the spatial and the temporal redundancies, which exist in the wavelet domain representation of a video signal, are exploited. Using simple and closed form expressions, the temporal information in the wavelet domain is first decorrelated in order to minimize the redundancy. The decorrelated noise-free coefficients are then modeled using a generalized Gaussian prior. For spatial filtering of the noisy wavelet coefficients, a new, low-complexity wavelet shrinkage method, which utilizes the correlation that exists between subsequent resolution levels, is proposed. Experimental results show that the proposed scheme outperforms state-of-the-art spatio-temporal filters in time and wavelet domains, both in terms of PSNR and visual quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it