De Noising

De-noising aims to remove unwanted noise from various data types, improving data quality and enabling more accurate analysis. Current research focuses on developing both supervised and unsupervised deep learning methods, including generative adversarial networks and adaptive filtering techniques, to address diverse noise types in images, spectra, and point clouds. These advancements are crucial for enhancing the reliability and accuracy of numerous applications, ranging from medical imaging (e.g., photoacoustic microscopy) and autonomous driving (LiDAR data processing) to improving the safety and robustness of large language models. The development of training-set-free methods is a significant trend, addressing the limitations of data scarcity in many domains.