Low-Frequency Image Noise Removal Using White Noise Filter
Bibliographic record
Abstract
Image noise filters usually assume noise as white Gaussian. However, in a capturing pipeline, noise often becomes spatially correlated due to in-camera processing that aims to suppress the noise and increase the compression rate. Mostly, only high-frequency noise components are suppressed since the image signal is more likely to appear in the low-frequency components of the captured image. As a result, noise emerges as coarse grain which makes white (all-pass) noise filters ineffective, especially when the resolution of the target display is lower than the captured image. Denoising of image approximation in coarse scale has the advantage of removing low-frequency noise, however, lack of spatial resolution degrades the image quality. This paper presents an approach for a coarse-grain removal. Our approach utilizes existing white Gaussian noise filters to address low-frequency component of spatially correlated noises, employing pixel decoupling, local shrinkage, and soft thresholding. Subjective and objective results show that the proposed approach better handles low-frequency noise compared to related work.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".