Video super resolution using contourlet transform and bilateral total variation filter
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
This paper introduces a new approach for video super resolution problem. To this end Compressive Sensing (CS) theory along with contourlet transform has been used. In CS framework the signal is assumed to be sparse in a transform domain. An approach has been suggested using this fact in which contourlet domain is used as the transform domain and a CS algorithm helps to find the high resolution frame. A post processing step is applied afterward to the estimated outputs to increase the quality. The post processing step consists of a deblurring term and a Bilateral Total Variation (BTV) filter for increasing the consistency. This method helps to relax the conditions on hardware and increase the quality of the video after capturing, in fact the quality of the video streams in consumer applications can be increased even the capturing device represents the scene in a low resolution format. Experimental results show significant improvement over existing super resolution methods in both objective and subjective quality.
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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