Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution

Unsupervised real world super resolution (USR) aims at restoring high-resolution (HR) images given low-resolution (LR) inputs when paired data is unavailable. One of the most common approaches is synthesizing noisy LR images using GANs and utilizing a synthetic dataset to train the model in a supervised manner. The goal of modeling the degradation generator is to approximate the distribution of LR images given a HR image. Previous works simply assumed the conditional distribution as a delta function and learned the deterministic mapping from HR image to a LR image. Instead, we propose the probabilistic degradation generator. Our degradation generator is a deep hierarchical latent variable model and more suitable for modeling the complex distribution. Furthermore, we train multiple degradation generators to enhance the mode coverage and apply the novel collaborative learning. We outperform several baselines on benchmark datasets in terms of PSNR and SSIM and demonstrate the robustness of our method on unseen data distribution.