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.

翻译：我们研究了为图像超分辨率问题生成多样化解的问题。从概率角度来看，这可以通过从逆问题的后验分布中采样来实现，这需要定义高分辨率图像的先验分布。本文提出使用预训练的分层变分自编码器（HVAE）作为先验。我们训练一个轻量级随机编码器，将低分辨率图像编码到预训练HVAE的潜在空间中。在推理阶段，我们结合低分辨率编码器与预训练生成模型来实现图像超分辨率。在面部超分辨率任务中，我们证明该方法在条件归一化流技术的计算效率与基于扩散方法的样本质量之间提供了有利的权衡。