Locally adaptive space variant restoration of CT images
Bibliographic record
Abstract
Summary form only given. An iterative algorithm for space variant image restoration was applied to computed tomographic (CT) images for improving spatial resolution compromised due to the finite size of the X-ray beam profiles. A prewhitening filter is designed from the noise power spectrum to uncorrelate the noise in the image. The restoration problem is formulated as a maximum-likelihood solution regularized with the weighted image norm. The nonstationary variance of the noise at each pixel location is utilized to locally regulate the smoothness or sharpness of the restored images. Unlike previous space variant restoration methods, this restoration procedure is carried out in a Cartesian coordinate system. Therefore, the symmetries in the spatial variations of the point spread function (PSF) are utilized to develop an efficient storage scheme which requires 0.75 megawords of memory for a 256*256 image, and a 9*9 PSF. This makes the computation of the reblurring process as fast as for a space invariant PSF.>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".