Wednesday, 01 December 2021, 14:00 at Virtual (Zoom details under abstract) – Deep-learning approaches to denoising achieve impressive results when trained on standard image-processing datasets in a supervised fashion. However, unleashing their potential in practice will require developing unsupervised or semi-supervised approaches capable of learning from real data, as well as understanding the strategies learned by these models to perform denoising. In …