Image Enhancement

Image enhancement aims to improve the visual quality and information content of images degraded by various factors like noise, low light, or artifacts. Current research heavily utilizes deep learning, employing architectures such as generative adversarial networks (GANs), diffusion models, and transformers, often incorporating physics-based modeling to improve generalizability and handle diverse degradation types. These advancements are crucial for improving the accuracy of downstream tasks in diverse fields, including medical imaging (e.g., CT and ultrasound), autonomous vehicles (e.g., radar image enhancement), and remote sensing (e.g., satellite imagery), as well as enhancing the visual appeal and usability of images in general.