Prior Image-Constrained Reconstruction using Style-Based Generative Models
By Varun A. Kelkar, Mark A. Anastasio. 2021
32 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Media Technology, Generative Models
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Citations in the corpus
- GAN Inversion: A Survey (2021, background): “, PULSE [35], ILO [36], or PICGM [37], also rely on a pretrained generator to solve inverse problems such as inpainting, super-resolution, or denoising.”
- Robust Compressed Sensing MRI with Deep Generative Priors (2021, background): “[51] Varun A Kelkar and Mark A Anastasio.”