Two-step super-resolution technique using bounded total variation and bisquare M-estimator under local illumination changes
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a super-resolution (SR) technique for images having arbitrarily-shaped local illumination changes. These variations tend to degrade the performance of the image registration, and hence impact the SR image reconstruction. Conventional SR techniques focus on enhancing the reconstruction step assuming aligned images. In this paper, we exploit our recent registration approach of images having illumination variations using a robust bisquare M-estimation. Then, we extend a bounded total variation-based approach for upsampling single frames to super-resolving multi-frames in order to reconstruct the unknown high-resolution (HR) frame. The proposed SR technique shows clear improvements over competing techniques in terms of objective metrics using simulated and real image pairs with illumination variations.
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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 | 0.002 |
| 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