Artifact Reduction

Artifact reduction focuses on removing unwanted distortions and noise from images and signals across various domains, improving data quality for analysis and downstream applications. Current research emphasizes deep learning approaches, particularly generative models like GANs and diffusion models, and multi-stage methods that leverage different image domains for optimal artifact removal. These advancements are crucial for improving the accuracy and reliability of medical imaging (e.g., CT, MRI), enhancing speech recognition, and improving the quality of generated images in computer vision.