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.
@article{arxiv.2102.12525,
title = {Prior Image-Constrained Reconstruction using Style-Based Generative Models},
author = {Varun A. Kelkar and Mark A. Anastasio},
journal= {arXiv preprint arXiv:2102.12525},
year = {2021}
}
Comments
Accepted for publication at the International Conference on Machine Learning (ICML) 2021