Bridging the Resolution Gap: Super-Resolution Enhancement of Panoramic Radiographs to Periapical-Like Clarity Using Deep Learning

Abstract

Panoramic radiographs (PANs) are widely used in dental practice for their broad coverage of the maxillofacial region, low cost, and clinical convenience. However, compared with periapical radiographs (PAs), PANs offer lower spatial resolution and reduced visibility of fine anatomical structures, including the lamina dura, periodontal ligament space, root morphology, and subtle periapical changes, which can limit their diagnostic utility for precision-dependent evaluations. Developing a super-resolution (SR) and enhancement method to enhance PAN images toward PA-like anatomical clarity could ultimately aid in early detection and prevention of potential diseases and reduce the need for additional intraoral imaging. A major challenge in developing an SR solution to align the level of detail in PAN to the PA standard is the lack of paired PAN and PA images that depict the same anatomy at their own levels of detail. A two-step method was designed to solve this problem. First, the image style translation model CycleGAN was used to transform HR PA images into synthetic PAN-style counterparts that simulate panoramic-like degradation in contrast, noise, and detail level. These generated images were paired with their original PA to create supervised training data for SR training. Next, two SR architectures, SwinIR and NAFNet, were then trained and evaluated using quantitative image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), and mean squared error (MSE). Both models demonstrated strong reconstruction performance and successfully recovered finer anatomical structures from degraded PAN-styled inputs. NAFNet achieved better overall performance and produced outputs that have both a quantitatively higher score and a closer visual appearance to the ground truth PA images. These results suggest that the proposed framework is a viable approach for enhancing dental radiographic detail and improving anatomical visibility in PANs. Although further validation on real PAN images with clinically verified reference labels is still needed, this work represents an important first step toward enhancing the anatomical detail visible in panoramic radiographs. With future clinical validation and expert assessment, this framework may help improve PAN-based screening and expand the diagnostic value of routinely acquired dental imaging.