  Image super-resolution (SR) techniques are used to generate a high-resolution
image from a low-resolution image. Until now, deep generative models such as
autoregressive models and Generative Adversarial Networks (GANs) have proven to
be effective at modelling high-resolution images. VAE-based models have often
been criticised for their feeble generative performance, but with new
advancements such as VDVAE, there is now strong evidence that deep VAEs have
the potential to outperform current state-of-the-art models for high-resolution
image generation. In this paper, we introduce VDVAE-SR, a new model that aims
to exploit the most recent deep VAE methodologies to improve upon the results
of similar models. VDVAE-SR tackles image super-resolution using transfer
learning on pretrained VDVAEs. The presented model is competitive with other
state-of-the-art models, having comparable results on image quality metrics.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing