Transfer Learning-Based Super-Resolution for High-Precision Medical Imaging

High-resolution medical images are critical for preserving intricate anatomical details essential for accurate diagnosis, effective surgical planning, and creating precise digital twins. However, acquiring such images often requires expensive equipment, specialized personnel, considerable time, and significant financial investment. This research addresses these challenges by proposing an AI-driven specialized super-resolution (SR) framework tailored for medical imaging. Our approach leverages transfer learning by fine-tuning high-performing general-domain SR models (BSRGAN, DPSR, HAT, RealESRGAN, and SwinIR) using approximately 190,000 images from KISTI’s Digital Korean dataset. On average, the fine-tuned SR models exhibited a 3.28% improvement in PSNR and a 0.6% increase in SSIM compared to their zero-shot counterparts, underscoring the effectiveness of transfer learning in enhancing both image quality and structural fidelity for medical applications. Notably, the fine-tuned HAT model achieved the highest performance, recording a Peak Signal-to-Noise Ratio (PSNR) of 39.95 and a Structural Similarity Index Map (SSIM) of 0.9461. The DPSR model also demonstrated robust detail preservation, with a PSNR of 39.60 and an SSIM of 0.9421. Qualitative evaluations by anatomy and medical imaging specialists confirmed the enhanced quality of the high-resolution outputs. These advancements highlight the transformative potential of specialized SR models to overcome the limitations of general-domain approaches in medical imaging. By laying the groundwork for developing high-resolution medical datasets, the proposed framework supports more precise diagnostics and advances the creation of accurate 3D human models and digital twins. Overall, this work underscores the vital role of AI-driven innovation in shaping the future of medical imaging and precision healthcare.