mlsanityData to 7 Oct 2026

Inverse problem regularization with hierarchical variational autoencoders

arXiv 2303.11217 ↗First posted 20 Mar 2023cs.CV cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 44 of its 62 references that are in the map. First posted on arXiv 20 Mar 2023.

Builds on

44 of 62 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 1.

6 papers.

  1. 2025LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimizationcited by 14
  2. 2025Good Things Come in Pairs: Paired Autoencoders for Inverse Problemscited by 5
  3. 2023Variational Bayes image restoration with compressive autoencoderscited by 4
  4. 2024Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overviewcited by 1
  5. 2024Deep priors for satellite image restoration with accurate uncertaintiescited by 1
  6. 2026SP3: Spherical Priors for Plug-and-Play Restorationcited by 1