Simultaneous Demosaicing and Chromatic Aberration Correction through Spectral Reconstruction
Bibliographic record
Abstract
We present an algorithm for simultaneously demosaicing digital images, and correcting chromatic aberration, that operates in terms of spectral bands. Chromatic aberration depends on both the camera’s optical system, and on the spectral characteristics of the light entering the camera. Previous works on calibrating chromatic aberration produce models of chromatic aberration that assume fixed relationships between image channels, an assumption that is only valid when the image channels capture narrow regions of the electromagnetic spectrum. When the camera has wideband channels, as is the case for conventional trichromatic (RGB) cameras, the aberration observed both within and between channels can only be accurately predicted given the spectral irradiance of the theoretical, aberration-free image. For an RGB camera, we use bandpass-filtered light to calibrate its chromatic aberration in terms of image position and light wavelength. Inspired by literature on reconstructing spectral images from RGB images, we then correct images for chromatic aberration by estimating aberration-free, spectral images. As we model within-channel chromatic aberration, our reconstructed images are sharper than those obtained by calibrated warping of color channels, yet we avoid artifacts commonly produced by explicit deblurring algorithms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".