Authors:
Jean Prost
1
;
Antoine Houdard
2
;
Andrés Almansa
3
and
Nicolas Papadakis
4
Affiliations:
1
Univ. Bordeaux, Bordeaux IMB, INP, CNRS, UMR 5251, F-33400 Talence, France
;
2
Ubisoft La Forge, F-33000 Bordeaux, France
;
3
Université Paris Cité, CNRS, MAP5, F-75006 Paris, France
;
4
Univ. Bordeaux, CNRS, INRIA, Bordeaux INP, IMB, UMR 5251, F-33400 Talence, France
Keyword(s):
Diverse Image Super-Resolution, Hierarchical Variational Autoencoder, Conditional Generative Model.
Abstract:
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.