CMX Lunch Seminar

Deep-learning models for image denoising achieve impressive results when trained on standard natural-image 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. In this talk, we will describe advances in this direction motivated by a real-world scientific application: determining the 3D atomic structure of catalytic nanoparticles from extremely noisy electron-microscope data.