In this talk, we look at a framework to obtain a useful estimate of an object semantically related to another object’s prior image, from highly incomplete imaging measurements using a style-based generative model. Obtaining useful information from incomplete imaging measurements of an object or ill-posed imaging inverse problem remains a “holy grail” of imaging science. We look into its details in the course of the talk.
Additional resources: https://arxiv.org/abs/2102.12525
No slides are available for this talk.