Learning based single image super-resolution (SISR) for real-world images has been an active research topic yet a challenging task, due to the lack of paired low-resolution (LR) and high-resolution (HR) training images. Most of the existing unsupervised real-world SISR methods adopt a twostage training strategy by synthesizing realistic LR images from their HR counterparts first, then training the super-resolution (SR) models in a supervised manner. However, the training of image degradation and SR models in this strategy are separate, ignoring the inherent mutual dependency between downscaling and its inverse upscaling process. Additionally, the ill-posed nature of image degradation is not fully considered. In this paper, we propose an image downscaling and SR model dubbed as SDFlow, which simultaneously learns a bidirectional manyto- many mapping between real-world LR and HR images unsupervisedly. The main idea of SDFlow is to decouple image content and degradation information in the latent space, where content information distribution of LR and HR images is matched in a common latent space. Degradation information of the LR images and the high-frequency information of the HR images are fitted to an easy-to-sample conditional distribution. Experimental results on real-world image SR datasets indicate that SDFlow can generate diverse realistic LR and SR images both quantitatively and qualitatively.

中文翻译：

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学习多对多映射以实现不成对的真实世界图像超分辨率和缩小尺寸


由于缺乏配对的低分辨率（LR）和高分辨率（HR）训练图像，基于学习的真实世界图像的单图像超分辨率（SISR）一直是一个活跃的研究课题，但也是一项具有挑战性的任务。大多数现有的无监督真实 SISR 方法采用两阶段训练策略，首先从 HR 图像中合成真实的 LR 图像，然后以监督方式训练超分辨率（SR）模型。然而，该策略中图像退化和SR模型的训练是分开的，忽略了降尺度及其逆升尺度过程之间固有的相互依赖性。此外，没有充分考虑图像退化的不适定性质。在本文中，我们提出了一种称为 SDFlow 的图像缩小和 SR 模型，它同时无监督地学习现实世界 LR 和 HR 图像之间的双向多对多映射。 SDFlow的主要思想是在潜在空间中解耦图像内容和退化信息，其中LR和HR图像的内容信息分布在公共潜在空间中匹配。将LR图像的劣化信息和HR图像的高频信息拟合到易于采样的条件分布。在真实世界图像 SR 数据集上的实验结果表明，SDFlow 可以定量和定性地生成各种真实的 LR 和 SR 图像。