A Novel Edge-Aware Bilateral Filter for Antialiasing and Denoising in Low-Dose CBCT Imaging
Bibliographic record
Abstract
Cone-Beam Computed Tomography (CBCT) is widely employed in medical and dental imaging. However, lowdose CBCT scans are prone to aliasing artifacts that distort microstructures, degrade image quality, and diminish diagnostic accuracy. Existing CBCT enhancement methods predominantly concentrate on noise reduction and edge preservation but are insufficient in effectively mitigating aliasing artifacts. This paper presents a novel edge-aware antialiasing and denoising method tailored for low-dose CBCT images. The proposed approach employs an improved bilateral filter that integrates antialiasing, denoising, and edge preservation into a unified framework. Unlike the traditional bilateral filter, it effectively reduces aliasing artifacts and noise in both soft tissue and bony structures while maintaining edge sharpness without over-smoothing. To refine the results and mitigate artifact amplification during edge sharpening, a median filter is applied following the bilateral filtering stage, further enhancing image quality. Subjective evaluations indicate that our method outperforms existing approaches, achieving a 98.05% improvement on average. Our approach effectively removes aliasing artifacts without introducing additional noise or artificial structures, enhancing the diagnostic reliability of CBCT images and addressing a significant gap in clinical practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".