Performance Benchmarking of Stochastic and Deterministic Representation Bases with Compressive Sensing in Computerized Tomography Images
Bibliographic record
Abstract
Computerized tomography is a procedure that emits X-rays rotating around the body, projecting signals to be processed and converted to images. X-rays radiation in people may cause cardiovascular diseases, malformations in prenatal babies, and increasing the development of cancer cells. The tomography device has coded-aperture to block some X-rays and it uses recovery computational techniques to getting a tomographic image from less radiation. One useful technique for such reconstruction is compressive sensing, which can recovery images from sparse signals. Usually, the sparsity of the images is obtained through transforming it into some basis matrix. This work compares from computational models the performance of two representation bases: one deterministic and one stochastic. The tomography images dataset was represented in every one of the bases and compressive sensing was applied to decreasing the information contained in each image. Then we apply the GPSR algorithm to reconstruction. Results showed that: both representation bases combined with compressive sensing reduce the samples number of the image available for its reconstruction without significantly affecting its quality. Also, the stochastic base presented a better performance concerning the Peak Signal to Noise Ratio (PSNR), this is 4.815% higher than the deterministic counterpart. On the other hand, it was identified that the image reconstruction is possible from 50 % or higher of the compression, i.e., the minimal samples percentage required for reconstruction is 50 %. We conclude that the stochastic base outperforms the deterministic equivalent mainly regarding quality image reconstructed while the differences considering the computational time and samples nurmber are not significantly,
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".