ArticleBMC medical imaging2026
Super-resolution of periapical and bitewing digital radiographs using convolutional neural network.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND AND
aimDue to the insufficient data and the ongoing development of machine learning (ML), this study was conducted to examine a deep learning approach for enhancing the resolution of dental Bite-wing (BW) and Peri-Apical (PA) Radiographs (Rg) based on Super Resolution (SR) theory. METHODS AND MATERIALS: 1000 BW, and PA Rg were collected: 750 images for training while 250 images for test. At first step, we downscaled all High Resolution (HR) images to create Low Resolution (LR) ones using 4*4 average pooling without overlap. Thereafter, we incorporated three deep learning-based super-resolution approaches and the most efficient one (down sampled skip-connection/ Multi-scale (DSC/MS)) was chosen. After training, our ML algorithm was tested by the 250 LR images incorporated six evaluation metrics.
resultsAfter five-time repletion of our model, the mean ± S.D of R
conclusionOur SR model demonstrated significant effectiveness and the DSC/MS showed noticeably superior results comparing to linear, cubic, or nearest neighbor interpolations.
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