  Obtaining an accurate and reliable estimate of an object from highly
incomplete imaging measurements remains a holy grail of imaging science. Deep
learning methods have shown promise in learning object priors or constraints to
improve the conditioning of an ill-posed imaging inverse problem. In this
study, a framework for estimating an object of interest that is semantically
related to a known prior image, is proposed. An optimization problem is
formulated in the disentangled latent space of a style-based generative model,
and semantically meaningful constraints are imposed using the disentangled
latent representation of the prior image. Stable recovery from incomplete
measurements with the help of a prior image is theoretically analyzed.
Numerical experiments demonstrating the superior performance of our approach as
compared to related methods are presented.
