  The difficulty of obtaining paired data remains a major bottleneck for
learning image restoration and enhancement models for real-world applications.
Current strategies aim to synthesize realistic training data by modeling noise
and degradations that appear in real-world settings. We propose DeFlow, a
method for learning stochastic image degradations from unpaired data. Our
approach is based on a novel unpaired learning formulation for conditional
normalizing flows. We model the degradation process in the latent space of a
shared flow encoder-decoder network. This allows us to learn the conditional
distribution of a noisy image given the clean input by solely minimizing the
negative log-likelihood of the marginal distributions. We validate our DeFlow
formulation on the task of joint image restoration and super-resolution. The
models trained with the synthetic data generated by DeFlow outperform previous
learnable approaches on all three datasets.
