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The difficulty of obtaining paired data remains a major bottleneck forlearning image restoration and enhancement models for real-world applications.Current strategies aim to synthesize realistic training data by modeling noiseand degradations that appear in real-world settings. We propose DeFlow, amethod for learning stochastic image degradations from unpaired data. Ourapproach is based on a novel unpaired learning formulation for conditionalnormalizing flows. We model the degradation process in the latent space of ashared flow encoder-decoder network. This allows us to learn the conditionaldistribution of a noisy image given the clean input by solely minimizing thenegative log-likelihood of the marginal distributions. We validate our DeFlowformulation on the task of joint image restoration and super-resolution. Themodels trained with the synthetic data generated by DeFlow outperform previouslearnable approaches on three recent datasets. Code and trained models areavailable at: https://github.com/volflow/DeFlow

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows