论文标题
多样化的超分辨率，预处理的深层层次vaes
Diverse super-resolution with pretrained deep hiererarchical VAEs
论文作者
论文摘要
我们研究了为图像超分辨率问题产生多种解决方案的问题。从概率的角度来看，这可以通过从反问题的后验分布中取样来完成，这需要在高分辨率图像上定义先前的分布。在这项工作中，我们建议将验证的层次变分自动编码器（HVAE）作为先验。我们训练一个轻巧的随机编码器，以在预处理的HVAE的潜在空间中编码低分辨率的图像。在推断时，我们将低分辨率编码器和预验证的生成模型结合在一起，以超级溶解图像。我们在面部超分辨率的任务上证明了我们的方法在有条件归一化流的计算效率与基于扩散的方法的样本质量之间提供了有利的权衡。
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.
