Super-resolution based on interpolation and global sub pixel translation
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
In this paper we present a new class of reconstruction algorithms that are basically different from the traditional approaches. We deviate from the traditional technique which treats the pixels of the image as point samples. In this work, the pixels are treated as rectangular surface samples. It is in conformity with image formation process, in particular for CCD/CMOS sensors, which are a matrix of rectangular surfaces sensitive to the light. We show that results of better quality in terms of the measurements employed are obtained by formulating the reconstruction as a two-stage process: the restoration of image followed by the application of the point spread function (PSF) of the imaging sensor. By coupling the PSF with the reconstruction process, we satisfy a measure of accuracy that is based on the physical limitations of the sensor. Effective techniques for the restoration of image are derived to invert the effects of the PSF and estimate the original image. For the algorithm of restoration, we introduce a new method of interpolation implying a sequence of images, not necessarily a temporal sequence, shifted compared to an image of reference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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