Comprehensive Analysis on the Effects of Noise Estimation Strategies on Image Noise Artifact Suppression Performance
Bibliographic record
Abstract
In this paper, the effects of employing different noise estimation strategies on the performance of noise artifact suppression techniques in achieving high image quality has been investigated. Most literature on the subject tends to use the true noise level of the noisy image when performing noise artifact suppression. However, this approach does not reflect how such techniques would be used in practical situations where the true noise level is unknown, which is common in most image and video processing applications. Therefore, in practical situations, the noise level must first be estimated before a noise artifact suppression technique can be applied using the estimated noise level. Through a comprehensive analysis of different noise estimation strategies using empirical testing on a variety of images with different characteristics, the MAD wavelet noise estimation technique was found to be the overall preferred noise estimation technique for all popular noise artifact suppression techniques investigated (BM3D, bilateral, Neigh Shrink, BLS-GSM and non-local means). Furthermore, the BM3D noise artifact suppression technique, combined with the MAD wavelet noise estimation technique, was found to offer the best performance in achieving high image quality in situations where the noise level is unknown and must be estimated. The outcome of this research is clear recommendations that can be used in practise when suppressing noise artifacts exhibited in digital imagery and video.
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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 | 0.004 | 0.020 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".