Data adaptive filters for demosaicking: a framework
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
A new demosaicking framework for single-sensor imaging devices operating on a Bayer color filter array (CFA) is introduced and analyzed. An efficient data adaptive filtering concept in conjunction with the refined spectral models constitutes the base for the proposed framework. Using a different form of the function mapping the aggregated absolute differences among the CFA inputs to the edge-sensing weighting coefficients, the framework allows to design fully automated demosaicking solutions suitable for common digital imaging apparatus, and alternatively, the proposed solutions can also be used to support PC-based demosaicking of the raw CFA images. Thus, the framework can be seen as a universal tool satisfying the needs of the end-users for i) the instant access and visualization of the captured images, and ii) the interactive processing of the raw sensor data. Moreover, the proposed framework is relatively easy to implement in either software or hardware. Experimental results indicate that the proposed framework exhibits excellent performance in terms of the commonly used objective criteria and at the same time it produces demosaicked images with impressive visual quality.
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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 | 0.001 |
| 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