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 three recent datasets. Code and trained models are
available at: this https URL

DeFlow 是一种用于生成逼真的训练数据的方法，用于学习来自未配对数据的随机图像退化，可广泛应用于图像恢复和增强模型的实际场景，通过条件归一化流的无配对学习实现，基于共享流编码器 - 解码器网络的潜在空间建模降级过程，从而学习给定干净输入的噪声图像的条件分布。在三个近期数据集上测试后，其用合成数据训练的模型优于以前学习方法的模型。