Prior Image-Constrained Reconstruction using Style-Based Generative Models

Varun A. Kelkar · Mark Anastasio


Keywords: [ Kernel Methods ] [ Sparsity and Compressed Sensing ] [ Algorithms ] [ Frequentist Statistics ]

[ Abstract ]
[ Slides
[ Paper ]
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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.

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