SSIM-based non-local means image denoising
Why this work is in the frame
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Bibliographic record
Abstract
Perceptually inspired image processing has been an emerging field of study in recent years. Here we make one of the first efforts to incorporate the structural similarity (SSIM) index, a successful perceptual image quality assessment measure, into the framework of non-local means (NLM) image denoising, which is a state-of-the-art method that delivers superior desnoising performance. Specifically, a denoised image patch is obtained by weighted averaging of neighboring patches, where the similarity between patches as well as the weights assigned to the patches are determined based on an estimation of SSIM. A two-stage approach is proposed for robust SSIM estimation in the presence of noise. Moreover, motivated by the ideas behind SSIM, we adjust the contrast and mean of each patch before feeding it into the weighted averaging process. Our experimental results show that the proposed SSIM-based NLM algorithm achieves better SSIM and PSNR performance and provides better visual quality than least square based NLM method.
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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 | 0.001 | 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 | 0.001 | 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