Visual Quality Enhancement of Low-Dose Dental CBCT Images
Bibliographic record
Abstract
Cone-Beam Computed Tomography (CBCT) provides superior image quality in dentistry, but involves higher radiation exposure. To address this, lower radiation levels in CBCT imaging are used, resulting in higher noise levels. Although numerous efforts have been made to improve the visual quality of low-dose CBCT images, they often fail to achieve an optimal balance between noise reduction and edge preservation. This paper introduces a novel denoising algorithm surpassing previous methods, aimed at enhancing diagnostic accuracy by improving visual quality. Our approach begins with an adaptive median filter, followed by a fusion of frequency and spatial domain filtering techniques, supplemented by nonlocal means (NLM) smoothing and Laplacian sharpening. Subjective evaluations show a significant performance improvement over current state-of-the-art methods, marking a substantial progress toward achieving visual fidelity parity between low and high radiation CBCT scans.
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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) | 0.003 | 0.001 |
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".