Image Super-Resolution With Deep Variational Autoencoders

TL;DR

This paper introduces VDVAE-SR, a deep VAE-based model for image super-resolution that leverages transfer learning and recent advancements to achieve competitive high-resolution image generation results.

Contribution

The paper presents VDVAE-SR, a novel super-resolution model using transfer learning on pretrained deep VAEs, outperforming previous VAE-based approaches.

Findings

Competitive image quality metrics compared to state-of-the-art models

Utilizes transfer learning on pretrained VDVAEs for super-resolution

Demonstrates potential of deep VAEs in high-resolution image generation

Abstract

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.