Artifact Restoration

Artifact restoration focuses on removing unwanted distortions or anomalies from various data types, including images, videos, and text, to improve data quality and analysis. Current research emphasizes the use of deep learning models, such as diffusion probabilistic models, generative adversarial networks (GANs), and vision transformers, often incorporating techniques like attention mechanisms and meta-learning to enhance efficiency and generalization across different artifact types. These advancements are crucial for improving the accuracy of computer-aided diagnosis in medicine, enhancing the reliability of remote sensing technologies, and boosting the performance of natural language processing models by mitigating biases in training data.