Residual Noise

Residual noise, unwanted signals or artifacts remaining after initial processing, is a persistent challenge across diverse scientific and engineering domains. Current research focuses on developing advanced algorithms, including deep learning models like diffusion probabilistic models and vision transformers, to effectively remove this noise from various data types such as audio, images, and even thermal fields in manufacturing processes. These efforts aim to improve the accuracy and reliability of analyses and predictions in fields ranging from speech enhancement and exoplanet detection to additive manufacturing and material science, ultimately leading to more robust and efficient technologies.