Diverse super-resolution with pretrained deep hiererarchical VAEs

Understand

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.