Learned imaging with constraints and uncertainty quantification

TitleLearned imaging with constraints and uncertainty quantification
Publication TypePresentation
Year of Publication2019
AuthorsFelix J. Herrmann, Ali Siahkoohi, Gabrio Rizzuti
Keywordsdeep learning, Expectation Maximization, HotCSE, Imaging, Uncertainty quantification

We outline new approaches to incorporate ideas from convolutional networks into wave-based least-squares imaging. The aim is to combine hand-crafted constraints with deep convolutional networks allowing us to directly train a network capable of generating samples from the posterior. The main contributions include combination of weak deep priors with hard handcrafted constraints and a possible new way to sample the posterior.



Citation Keyherrmann2019HOTCSEliwcuq