Joint Demosaicking and Blind Deblurring Using Deep Convolutional Neural Network
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
Despite extensive research efforts, blind image deblurring remains a challenge without general robust solutions. A long-overlooked problem of existing deblurring methods is that they are all designed to work on fully sampled RGB input images for simplicity. But, in practice, most RGB color images are reconstructed from Bayer mosaic data hence riddled with various high-frequency demosaicking artifacts, such as zippering and moiré patterns, which can easily derail a deblurring algorithm. In this paper, we propose a novel multi-scale deep convolutional neural network to solve demosaicking and deblurring jointly. By processing Bayer raw images directly, our method is free of the interference of demosaicking artifacts. Extensive experiments show that the joint approach greatly outperforms the simple cascade of state-of-art demosaicking and deblurring methods.
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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 | 0.000 | 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 | 0.000 | 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