Prior Image-Constrained Reconstruction using Style-Based Generative Models

Varun A. Kelkar · Mark Anastasio

[ Abstract ] [ Livestream: Visit Sparsity and Compressed Sensing ] [ Paper ]
Wed 21 Jul 7:40 a.m. — 7:45 a.m. PDT
[ Paper ]

Obtaining a useful 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.

Chat is not available.