Title: Improving dental X-ray image resolution with deep learning-based super-resolution techniques

Authors: Vaishali Patel; Anand Mankodia

Addresses: U.V. Patel College of Engineering, Ganpat University, India ' BAPS Swaminarayan Vidyamandir, Near Ganpat University, India

Abstract: Although the dental X-rays are useful for detecting and treating oral health problems, the low-resolution images they produce often make it hard for dental professionals to see fine details. This limitation occasionally leads to diagnostic challenges and even results in missed problems. As a means of addressing this issue, our study explored the application of deep learning approaches to sharpen and improve the quality of dental X-ray pictures, making them clearer and easier to interpret. We applied several deep learning methods, known for their success in enhancing image quality, to a dataset of dental X-rays. The results show significant improvements in clarity, with higher image quality scores – measured by metrics like PSNR and SSIM – that indicate a more detailed view of dental structures. These improvements could help dental professionals to catch issues earlier and make more accurate diagnoses. Our research demonstrates the potential of deep learning to change dental X-ray imaging, supporting better outcomes for patients and providing a useful tool for dental care providers.

Keywords: dental X-ray imaging; super-resolution; machine learning; deep learning; convolution neural network; medical imaging; image processing.

DOI: 10.1504/IJBET.2025.149619

International Journal of Biomedical Engineering and Technology, 2025 Vol.49 No.2, pp.169 - 187

Received: 05 Apr 2025
Accepted: 29 Jun 2025
Published online: 07 Nov 2025 *

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