Nonlinear demosaicking method and apparatus for nonlinear CMOS image sensors exhibiting low-density salt-and-pepper noise
Bibliographic record
Abstract
Simple demosaicking methods designed for linear CMOS image sensors, such as MATLAB’s demosaic function, may be used with a monotonic nonlinear sensor having a Bayer color filter array (CFA). However, such methods may be inadequate at handling dynamic salt-and-pepper noise (SPN), i.e., outlier pixels, which is expected in images taken with a nonlinear sensor. Although SPN is present with linear sensors, nonlinear sensors express low-density light-dependent SPN that requires filtering. Extending a recent work on dynamic SPN filtering of a nonlinear sensor, we propose, evaluate, and verify a nonlinear method and apparatus to demosaic images, taken with a Bayer CFA, while simultaneously filtering the SPN. The approach relies on the use of weighted medians to filter the SPN, especially at densities that imply isolated outliers in small neighborhoods, while determining an accurate red, green, and blue (RGB) color at every pixel location. For explanatory purposes, three variants of the proposed method are presented and evaluated. A ground-truth image set, in which RGB channels were not obtained by demosaicking, is subsampled in a Bayer CFA pattern to produce mosaicked images for testing. In varying densities, SPN is introduced to these Kodak images for method and apparatus evaluation. Results of the proposed method and its variants are compared with those obtained with MATLAB’s demosaic function. Considering the alternatives and also apparatus complexity, the proposed nonlinear demosaicking method proves superior in visual quality, for smooth textures and along edges, and in peak signal-to-noise ratio, when there is low-density SPN.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".