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
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI
Ahmad Elawady, Gunjan Chhablani, Ram Ramrakhya, Karmesh Yadav, Dhruv Batra, Zsolt Kira, Andrew Szot
Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models
Seyedmorteza Sadat, Otmar Hilliges, Romann M. Weber