Quantitative and Qualitative Evaluation of a Confidence-Aware Transformer-Based Super-Resolution Framework for Panoramic Radiographs

OBJECTIVES: This study aimed to develop and evaluate a confidence-aware transformer-based super-resolution framework, termed CAT-PRSR, to enhance image quality and diagnostic reliability in panoramic dental radiographs. METHODS: A total of 1078 anonymised panoramic radiographs were retrospectively collected (950 for training, 128 for testing). The CAT-PRSR framework integrating a transformer-based SR backbone with a confidence-aware training strategy was developed. The model generates a high-resolution output with pixel-wise uncertainty estimation, allowing adaptive learning focused on diagnostically relevant regions while minimising over-enhancement in noise-sensitive areas. Model performance was evaluated using 6 quantitative metrics - peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), spatial correlation coefficient (SCC), natural image quality evaluator (NIQE), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID)-and mean opinion score (MOS) assessment. Based on quantitative performance, 4 representative state-of-the-art SR models were selected for comparison at 4×, 6×, and 8× magnifications. RESULTS: CAT-PRSR demonstrated superior performance across all metrics and magnification levels. It achieved the highest peak signal-to-noise ratio (36.41 at 4 ×, 36.19 at 6 ×, and 33.73 at 8 ×) and the lowest FID (1.77, 9.29, and 2.09, respectively), outperforming all comparison models. In MOS evaluations, CAT-PRSR maintained diagnostic utility scores statistically comparable to ground truth images (P > .05), while other models showed significant degradation (P < .001). CONCLUSION: The proposed CAT-PRSR framework demonstrated potential to enhance panoramic radiograph resolution by integrating pixel-level fidelity with improved diagnostic reliability. CLINICAL RELEVANCE: The CAT-PRSR model may enhance the diagnostic reliability of panoramic radiographs acquired under low-resolution conditions, supporting more accurate clinical decision-making and serving as a reliable imaging resource for AI-driven dental research.