  In this paper, we propose to regularize ill-posed inverse problems using a
deep hierarchical variational autoencoder (HVAE) as an image prior. The
proposed method synthesizes the advantages of i) denoiser-based Plug \& Play
approaches and ii) generative model based approaches to inverse problems.
First, we exploit VAE properties to design an efficient algorithm that benefits
from convergence guarantees of Plug-and-Play (PnP) methods. Second, our
approach is not restricted to specialized datasets and the proposed PnP-HVAE
model is able to solve image restoration problems on natural images of any
size. Our experiments show that the proposed PnP-HVAE method is competitive
with both SOTA denoiser-based PnP approaches, and other SOTA restoration
methods based on generative models.
