Blind denoising using dense hybrid convolutional network
Bibliographic record
Abstract
Abstract The performance of existing deep convolutional networks is limited when encountering images with different noise levels. In this study, a denoising method with state‐of‐the‐art performance that combines a deep convolutional network with the traditional nonlocal mean denoising method is proposed. The noisy image is first denoised using the nonlocal mean method. Then, the denoised image is input into the proposed dense hybrid convolutional network to be trained, producing a clean image with clear details. The dense hybrid convolutional network comprises three parts: a feature‐extracting noise‐suppressing module that extracts abstract features from denoised images and suppresses the residual noise by interval convolution; a feature‐learning module used for training blurred edges and textures; and a magnifying module that uses deconvolution to restore the feature maps to the original size and reduce the noise again. In contrast to existing denoising algorithms, the method has two desirable properties: 1) it can restore edges and textures clearly while removing the noise; 2) it effectively deals with noise of unknown levels (i.e. blind denoising) with a single network model. The conducted experiments show that the proposed method achieves superior performance compared to those of state‐of‐the‐art denoising methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".