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

• URL: http://arxiv.org/abs/2101.05796v1
• Date: Thu, 14 Jan 2021 18:58:01 GMT
• Title: DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
• Authors: Valentin Wolf, Andreas Lugmayr, Martin Danelljan, Luc Van Gool, Radu Timofte
• Abstract summary: We propose DeFlow, a method for learning image degradations from unpaired data. We model the degradation process in the latent space of a shared flow-decoder network. We validate our DeFlow formulation on the task of joint image restoration and super-resolution.
• Score: 145.83812019515818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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