Separating Spectral and Spatial Feature Aggregation for Demosaicking
Bibliographic record
Abstract
Existing demosaicking neural models typically follow the same design principles as those adopted by the rest image restoration tasks; yet one unique & often overlooked problem with demosaicking is it also suffers from the moiré artifact. This observation inspires us to examine the key reason that moiré happens only to demosaicking instead of other image restoration tasks. In this process, We identify the spectral inconsistency concealed in the input of demosaicking neural networks; our findings also clarify the failure of the traditional dense spatiospectral feature aggregation in mitigating spectral inconsistency. Based on the analysis, a new solution is proposed to address moiré while preserving fine image details. In particular, we decouple the traditionally used spatio-spectral feature aggregation into comprehensive spectral aggregation and local spatial aggregation. Throughout a diverse range of experiments with quantitative and qualitative results, our approach is shown capable of significantly reducing artifacts such as moiré and over-smoothness, as well as drastically boosting state-of-the-art performance without increasing computational cost.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".