  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 two-stage 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
many-to-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.
