Unsupervised real world super resolution (USR) aims to restore
high-resolution (HR) images given low-resolution (LR) inputs, and its
difficulty stems from the absence of paired dataset. One of the most common
approaches is synthesizing noisy LR images using GANs (i.e., degradation
generators) and utilizing a synthetic dataset to train the model in a
supervised manner. Although the goal of training the degradation generator is
to approximate the distribution of LR images given a HR image, previous works
have heavily relied on the unrealistic assumption that the conditional
distribution is a delta function and learned the deterministic mapping from the
HR image to a LR image. In this paper, we show that we can improve the
performance of USR models by relaxing the assumption and propose to train the
probabilistic degradation generator. Our probabilistic degradation generator
can be viewed as a deep hierarchical latent variable model and is more suitable
for modeling the complex conditional distribution. We also reveal the notable
connection with the noise injection of StyleGAN. Furthermore, we train multiple
degradation generators to improve the mode coverage and apply collaborative
learning for ease of training. We outperform several baselines on benchmark
datasets in terms of PSNR and SSIM and demonstrate the robustness of our method
on unseen data distribution. Code is available at
this https URL

本研究提出一种概率降级生成器，用于训练无监督真实场景超分辨率模型，并通过使用多个生成器和协作学习来提高模型性能和稳健性，相关方法在基准数据集上表现出优异的性能。