Chromatic aberration correction: an enhancement to the calibration of low‐cost digital dermoscopes
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND/PURPOSE: We present a method for calibrating low-cost digital dermoscopes that corrects for color and inconsistent lighting and also corrects for chromatic aberration. Chromatic aberration is a form of radial distortion that often occurs in inexpensive digital dermoscopes and creates red and blue halo-like effects on edges. Being radial in nature, distortions due to chromatic aberration are not constant across the image, but rather vary in both magnitude and direction. As a result, distortions are not only visually distracting but could also mislead automated characterization techniques. METHODS: Two low-cost dermoscopes, based on different consumer-grade cameras, were tested. Color is corrected by imaging a reference and applying singular value decomposition to determine the transformation required to ensure accurate color reproduction. Lighting is corrected by imaging a uniform surface and creating lighting correction maps. Chromatic aberration is corrected using a second-order radial distortion model. RESULTS: Our results for color and lighting calibration are consistent with previously published results, while distortions due to chromatic aberration can be reduced by 42-47% in the two systems considered. CONCLUSION: The disadvantages of inexpensive dermoscopy can be quickly substantially mitigated with a suitable calibration procedure.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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