Enhanced Medical Image Super-Resolution Using GANs and Multi-Modal Fusion

In order to improve medical imaging techniques, specifically Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans, this work investigates the deployment of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN). An essential tool for enhancing the clarity, diagnostic value of medical images is ESRGAN, a cutting-edge deep learning model that is highly effective at reconstructing high-resolution (HR) images from their low-resolution (LR) counterparts. By combining Residual-in-Residual Dense Blocks (RRDB), adversarial loss, and perceptual loss, ESRGAN differs from conventional super-resolution techniques like SRCNN and may produce incredibly realistic and detailed textures while maintaining the clinical qualities necessary for medical diagnosis. The ESRGAN design ensures improved visualization of fine anatomical structures by efficiently extracting and reconstructing hierarchical features from medical pictures. Based on VGG feature maps, the model's application of perceptual loss guarantees that outputs preserve visual integrity while emphasizing important information required for a precise diagnosis. With an emphasis on processing CT and MRI images to increase image resolution without adding artifacts, the ESRGAN pipeline is specifically designed for medical imaging in this work. This study demonstrates how ESRGAN can help with treatment planning and diagnostic accuracy, opening the door for more sophisticated super-resolution medical imaging methods. Domain-specific optimizations for real-time healthcare applications will be investigated in future studies.