Ring Artifact
Ring artifacts, unwanted distortions in images or signals, hinder accurate interpretation and analysis across diverse fields, from medical imaging to AI-generated content. Current research focuses on developing methods to detect and remove these artifacts using techniques like conditional inpainting, adaptive projected guidance in diffusion models, and convolutional neural networks, often incorporating wavelet or spectral analysis to better isolate and target the artifacts. Successfully mitigating ring artifacts is crucial for improving the reliability of diagnostic tools, enhancing the quality of synthetic media, and advancing the accuracy of various machine learning applications.
Papers
Fact or Artifact? Revise Layer-wise Relevance Propagation on various ANN Architectures
Marco Landt-Hayen, Willi Rath, Martin Claus, Peer Kröger
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection
Md Awsafur Rahman, Bishmoy Paul, Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Shaikh Anowarul Fattah