Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging

Researchers have developed a self-supervised learning method that can reconstruct audio and images from clipped/saturated measurements without requiring ground truth training data. The approach extends self-supervised learning to non-linear inverse problems and performs nearly as well as fully supervised methods while using only clipped measurements for training.