Improving resolution of panoramic radiographs: super-resolution concept

Abstract

Abstract Objectives Dental 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. Methods About 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). Results SSIM 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. Conclusion The findings highlight the potential of super-resolution concepts to significantly improve the quality and detail of dental panoramic radiographs, thereby contributing to enhanced interpretability.