@inproceedings{c7d4d6cfb3054ee5be07acb8112432c0,
title = "Prior Image-Constrained Reconstruction using Style-Based Generative Models",
abstract = "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.",
author = "Kelkar, \{Varun A.\} and Anastasio, \{Mark A.\}",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 by the author(s); 38th International Conference on Machine Learning, ICML 2021 ; Conference date: 18-07-2021 Through 24-07-2021",
year = "2021",
language = "English (US)",
series = "Proceedings of Machine Learning Research",
publisher = "ML Research Press",
pages = "5367--5377",
booktitle = "Proceedings of the 38th International Conference on Machine Learning, ICML 2021",
}