Image Purification

Image purification aims to remove unwanted artifacts or noise from images, improving their quality and enabling more accurate analysis. Current research focuses on developing robust methods for removing various types of noise, including adversarial attacks and backdoor triggers, often employing deep learning models like autoencoders, diffusion models, and specialized neural networks for tasks such as denoising and segmentation. These advancements are crucial for improving the reliability of image-based analyses across diverse fields, from astronomy to medical imaging and cybersecurity, where corrupted images can hinder accurate interpretation and decision-making.