Scale Invariant Super-Resolutions Methods with Application to InSAR Images
Why this work is in the frame
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Bibliographic record
Abstract
Super-Resolution is the process of generating high-resolution (HR) images from a low-resolution (LR) ones. In learning-based SR algorithms, artificial neural networks (ANN) are used. This is achieved by training the network using HR and LR image pairs and use this network later to create new HR images from LR ones. Our work postulates that the scaling process is invariant across scales. Thus, a model trained at lower scales can be used to reconstruct higher resolution images when the ground truth is not available to train the model. We call this approach Scale Invariant Super-Resolution (SINV) We evaluated SINV using different datasets, and with different upscaling factors <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The upscaling factor is the factor by which the image resolution is increased. and showed that it outperforms conventional approaches. We have applied SINV to processing InSAR images.
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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 | 0.001 |
| 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