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.

该论文提出了 SDFlow，一个将单幅图像超分辨率问题转化为无监督学习的模型，通过在潜在空间中匹配 LR 和 HR 图像的内容信息分布来实现图像降尺度和超分辨率的自动学习。实验结果表明 SDFlow 能够生成多样且逼真的 LR 和 SR 图像。

学习无配对真实世界图像的多对多映射进行图像超分辨率和图像降采样

Learning Many-to-Many Mapping for Unpaired Real-World Image  Super-resolution and Downscaling

High-resolution digital images are usually downscaled to fit various display
screens or save the cost of storage and bandwidth, meanwhile the post-upscaling
is adpoted to recover the original resolutions or the details in the zoom-in
images. However, typical image downscaling is a non-injective mapping due to
the loss of high-frequency information, which leads to the ill-posed problem of
the inverse upscaling procedure and poses great challenges for recovering
details from the downscaled low-resolution images. Simply upscaling with image
super-resolution methods results in unsatisfactory recovering performance. In
this work, we propose to solve this problem by modeling the downscaling and
upscaling processes from a new perspective, i.e. an invertible bijective
transformation, which can largely mitigate the ill-posed nature of image
upscaling. We develop an Invertible Rescaling Net (IRN) with deliberately
designed framework and objectives to produce visually-pleasing low-resolution
images and meanwhile capture the distribution of the lost information using a
latent variable following a specified distribution in the downscaling process.
In this way, upscaling is made tractable by inversely passing a randomly-drawn
latent variable with the low-resolution image through the network. Experimental
results demonstrate the significant improvement of our model over existing
methods in terms of both quantitative and qualitative evaluations of image
upscaling reconstruction from downscaled images.

本文提出了一种新的、基于可逆双射转换的图像下采样和上采样建模方法，使用一种随机生成的潜向量来反向生成指定分布的下采样信息，提高了降采样图像的上采样表现。通过定量和定性的图像重建评估，证明了该方法在图像上采样重建方面相比现有方法有显著的改进。