  Super-resolution is an ill-posed problem, since it allows for multiple
predictions for a given low-resolution image. This fundamental fact is largely
ignored by state-of-the-art deep learning based approaches. These methods
instead train a deterministic mapping using combinations of reconstruction and
adversarial losses. In this work, we therefore propose SRFlow: a normalizing
flow based super-resolution method capable of learning the conditional
distribution of the output given the low-resolution input. Our model is trained
in a principled manner using a single loss, namely the negative log-likelihood.
SRFlow therefore directly accounts for the ill-posed nature of the problem, and
learns to predict diverse photo-realistic high-resolution images. Moreover, we
utilize the strong image posterior learned by SRFlow to design flexible image
manipulation techniques, capable of enhancing super-resolved images by, e.g.,
transferring content from other images. We perform extensive experiments on
faces, as well as on super-resolution in general. SRFlow outperforms
state-of-the-art GAN-based approaches in terms of both PSNR and perceptual
quality metrics, while allowing for diversity through the exploration of the
space of super-resolved solutions.
