Efficient Posterior Sampling For Diverse Super-Resolution with Hierarchical VAE Prior - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2024

Efficient Posterior Sampling For Diverse Super-Resolution with Hierarchical VAE Prior

Jean Prost
  • Fonction : Auteur
  • PersonId : 1084606
Antoine Houdard
Andrés Almansa

Résumé

We investigate the problem of producing diverse solutions to an image super-resolution problem. From a probabilistic perspective, this can be done by sampling from the posterior distribution of an inverse problem, which requires the definition of a prior distribution on the high-resolution images. In this work, we propose to use a pretrained hierarchical variational autoencoder (HVAE) as a prior. We train a lightweight stochastic encoder to encode low-resolution images in the latent space of a pretrained HVAE. At inference, we combine the low-resolution encoder and the pretrained generative model to super-resolve an image. We demonstrate on the task of face super-resolution that our method provides an advantageous trade-off between the computational efficiency of conditional normalizing flows techniques and the sample quality of diffusion based methods.
Fichier principal
Vignette du fichier
2205.10347.pdf (7.85 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03675314 , version 1 (24-01-2024)

Identifiants

Citer

Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis. Efficient Posterior Sampling For Diverse Super-Resolution with Hierarchical VAE Prior. VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications, Feb 2024, Rome, Italy. ⟨10.5220/0012352800003660⟩. ⟨hal-03675314⟩
131 Consultations
35 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More