PROST, Jean; HOUDARD, Antoine; ALMANSA, Andrés; PAPADAKIS, Nicolas

doi:10.5220/0012352800003660

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PROST, Jean
Institut de Mathématiques de Bordeaux [IMB]

HOUDARD, Antoine
Ubisoft

ALMANSA, Andrés
Mathématiques Appliquées Paris 5 [MAP5 - UMR 8145]

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Communication dans un congrès

Ce document a été publié dans

VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications, 2024-02-27, Rome.

Résumé en anglais

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.< Réduire