Multi-view video super-resolution for hybrid cameras using modified NLM and adaptive thresholding
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
Dual-mode (hybrid) cameras are able to simultaneously shoot two types of video streams: a high resolution with low frame rate stream and similarly a low resolution with high frame rate stream. There are some works on super-resolving the single camera video sequences. In this paper we propose a method for multi-view video super-resolution, utilizing sequences generated from a hybrid camera. In the proposed method we exploit the self-similarity in the spatial and temporal domains to reconstruct a pixel. We modify the nonlocal means method to be applied in our method. Through a combination of techniques including adaptive thresholds and specialized candidate pixel selection schemes, the proposed method reconstructs a high fidelity video stream with considerably improved performance.
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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