Feature Extraction for Patch Matching in Patch-based Denoising Methods
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
Patch-based image denoising is a popular topic in recent years. In existing works, the distance between two patches was calculated as their Euclidian distance. When the noise level is high, this approach may not be desirable in image denoising. In this paper, we propose to extract noise-robust feature vectors from image patches and match the image patches by their Euclidian distance of the feature vectors for grey scale image denoising. Our modification takes advantage of the fact that the mean of the Gaussian white noise is zero. For every patch in the noisy image, we use lines to divide the patch into two regions with equal area and we take the mean of the right region for each line. Hence, a number of features can be extracted. We use these extracted features to match the patches in the noisy image. By introducing feature-based patch matching, our method performs favourably for highly noisy images.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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