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arXiv · 2303.11217

Inverse problem regularization with hierarchical variational autoencoders

Jean Prost·Antoine Houdard·Andrés Almansa·Nicolas Papadakis
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Abstract

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.

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Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis. 2023-03-20. Inverse problem regularization with hierarchical variational autoencoders. https://doi.org/10.1109/iccv51070.2023.02093

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