Medical Image Enhancement

Medical image enhancement aims to improve the quality of medical images, addressing issues like poor illumination, noise, and low resolution, to facilitate more accurate diagnosis and treatment planning. Current research focuses on developing unsupervised and domain-adaptive methods, often employing generative adversarial networks (GANs) and transformer-based architectures, to enhance images without requiring large paired datasets or extensive retraining for different modalities. These advancements are significant because they improve the reliability and efficiency of diagnostic tools, potentially leading to better patient outcomes and more effective disease management across various medical imaging applications.