Low Resolution Medical Image Enhancement Using Generative Artificial Intelligence

Accurate diagnosis through medical image data is the base of proper prognosis in healthcare, which requires high-quality medical image. Enhancement of various low-resolution medical images in healthcare needs attention. The research addresses the challenge of generating high-resolution images from low-resolution inputs, a critical difficulty in picture super-resolution. Super-Resolution GAN (SRGAN), which enhances image quality through perceptual loss to produce visually realistic and high-resolution outputs, has been employed. Through experiments and evaluation, we demonstrated the effectiveness of SRGAN compared to other variants of GAN in producing realistic, high-resolution images, showcasing the advancements made in image generation tasks using GANs.