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Unsupervised Real-World Super-Resolution via Degradation Representations

Most current deep convolutional neural networks achieve great performance on single image super-resolution (SISR), trained with corresponding of high-resolution(HR) and low-resolution(LR) images. Conversely, the super-resolution performance of them is reduced testing in real-word super-resolution, since these methods create paired LR images by simple interpolation downsampling from HR images (e.g. bicubic) which is far more different from the natural degradation. In this paper, we design a novel unsupervised framework conditioned on degradation representations for real-world super-resolution. Our proposed method is composed of three stages: we first learn implicit degradation representations from real-world LR images and then acquire the LR images by downscaling network which would sharing a similar degradation with real-world images. Finally, we make paired data of the generated real LR images and HR images for training the SR network. Our model achieves better performance than recent SR methods on the NTIRE2020 real-world SR challenge Track1 dataset.