ArticleDento maxillo facial radiology2024
Improving resolution of panoramic radiographs: super-resolution concept.
Article in Dento maxillo facial radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Quantitative and Qualitative Evaluation of a Confidence-Aware Transformer-Based Super-Resolution Framework for Panoramic Radiographs.International dental journal · 2026Article
- Enhancing diagnostic quality in dental bitewings using transformer and GAN-Based image restoration.Oral radiology · 2026Article
- Super-resolution of periapical and bitewing digital radiographs using convolutional neural network.BMC medical imaging · 2026Article
- Deep learning-driven super-resolution for cone-beam computed tomography: AnImaging science in dentistry · 2026Article
- Panoramic image restoration and visual quality enhancement methods for digital art creation.Scientific reports · 2026Article
- Can super resolution via deep learning improve classification accuracy in dental radiography?Dento maxillo facial radiology · 2025Article
- Automatic detection of developmental stages of molar teeth with deep learning.BMC oral health · 2025Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesDental imaging plays a key role in the diagnosis and treatment of dental conditions, yet limitations regarding the quality and resolution of dental radiographs sometimes hinder precise analysis. Super-resolution with deep learning refers to a set of techniques used to enhance the resolution of images beyond their original size or quality using deep neural networks instead of traditional image interpolation methods which often result in blurred or pixelated images when attempting to increase resolution. Leveraging advancements in technology, this study aims to enhance the resolution of dental panoramic radiographs, thereby enabling more accurate diagnoses and treatment planning.
methodsAbout 1714 panoramic radiographs from 3 different open datasets are used for training (n = 1364) and testing (n = 350). The state of the art 4 different models is explored, namely Super-Resolution Convolutional Neural Network (SRCNN), Efficient Sub-Pixel Convolutional Neural Network, Super-Resolution Generative Adversarial Network, and Autoencoder. Performances in reconstructing high-resolution dental images from low-resolution inputs with different scales (s = 2, 4, 8) are evaluated by 2 well-accepted metrics Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR).
resultsSSIM spans between 0.82 and 0.98 while PSNR are between 28.7 and 40.2 among all scales and models. SRCNN provides the best performance. Additionally, it is observed that performance decreased when images are scaled with higher values.
conclusionThe findings highlight the potential of super-resolution concepts to significantly improve the quality and detail of dental panoramic radiographs, thereby contributing to enhanced interpretability.
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