  Normalizing flows have recently demonstrated promising results for low-level
vision tasks. For image super-resolution (SR), it learns to predict diverse
photo-realistic high-resolution (HR) images from the low-resolution (LR) image
rather than learning a deterministic mapping. For image rescaling, it achieves
high accuracy by jointly modelling the downscaling and upscaling processes.
While existing approaches employ specialized techniques for these two tasks, we
set out to unify them in a single formulation. In this paper, we propose the
hierarchical conditional flow (HCFlow) as a unified framework for image SR and
image rescaling. More specifically, HCFlow learns a bijective mapping between
HR and LR image pairs by modelling the distribution of the LR image and the
rest high-frequency component simultaneously. In particular, the high-frequency
component is conditional on the LR image in a hierarchical manner. To further
enhance the performance, other losses such as perceptual loss and GAN loss are
combined with the commonly used negative log-likelihood loss in training.
Extensive experiments on general image SR, face image SR and image rescaling
have demonstrated that the proposed HCFlow achieves state-of-the-art
performance in terms of both quantitative metrics and visual quality.
