MedSR-Vision: Deep Learning Framework for Multi-Domain Medical Image Super-Resolution

TL;DR

MedSR-Vision introduces a comprehensive deep learning framework for evaluating super-resolution models across multiple medical imaging modalities, enhancing diagnostic accuracy and guiding model selection.

Contribution

It provides a unified evaluation platform benchmarking three models across five medical imaging modalities at various magnifications, with insights for clinical application.

Findings

Real-ESRGAN offers superior perceptual quality at higher scales.

SwinIR preserves structural and diagnostic features effectively.

SRCNN is efficient and stable at lower magnifications.

Abstract

Medical image super-resolution (MedSR) is essential for improving diagnostic precision across diverse imaging modalities such as MRI, CT, X-ray, Ultrasound, and Fundus imaging. Despite rapid advances in deep learning, challenges remain in preserving anatomical accuracy, maintaining perceptual quality, and generalizing across medical domains. This paper presents MedSR-Vision, a novel unified deep learning framework for evaluating and comparing super-resolution models across five modalities: Brain MRI, Chest X-ray, Renal Ultrasound, Nephrolithiasis CT, and Spine MRI, at magnification scales of $\times2$, $\times3$, and $\times4$. Three representative models namely SRCNN, SwinIR, and Real-ESRGAN are benchmarked using multiple quantitative metrics encompassing fidelity, perceptual realism, and sharpness. Experimental analysis demonstrates that Real-ESRGAN achieves superior perceptual quality and edge recovery at higher scales, SwinIR excels in preserving structural and diagnostic features, and SRCNN provides efficient and stable performance at lower magnifications. The results establish domain-specific insights and practical guidelines for model selection in clinical imaging workflows, offering a standardized evaluation framework for future medical image super-resolution research and deployment.