Prior Image-Constrained Reconstruction using Style-Based Generative Models

Varun A Kelkar, Mark Anastasio
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:5367-5377, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-kelkar21a, title = {Prior Image-Constrained Reconstruction using Style-Based Generative Models}, author = {Kelkar, Varun A and Anastasio, Mark}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {5367--5377}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/kelkar21a/kelkar21a.pdf}, url = {https://proceedings.mlr.press/v139/kelkar21a.html}, 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.} }
Endnote
%0 Conference Paper %T Prior Image-Constrained Reconstruction using Style-Based Generative Models %A Varun A Kelkar %A Mark Anastasio %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-kelkar21a %I PMLR %P 5367--5377 %U https://proceedings.mlr.press/v139/kelkar21a.html %V 139 %X 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.
APA
Kelkar, V.A. & Anastasio, M.. (2021). Prior Image-Constrained Reconstruction using Style-Based Generative Models. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:5367-5377 Available from https://proceedings.mlr.press/v139/kelkar21a.html.

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