  We consider the single image super-resolution problem in a more general case
that the low-/high-resolution pairs and the down-sampling process are
unavailable. Different from traditional super-resolution formulation, the
low-resolution input is further degraded by noises and blurring. This
complicated setting makes supervised learning and accurate kernel estimation
impossible. To solve this problem, we resort to unsupervised learning without
paired data, inspired by the recent successful image-to-image translation
applications. With generative adversarial networks (GAN) as the basic
component, we propose a Cycle-in-Cycle network structure to tackle the problem
within three steps. First, the noisy and blurry input is mapped to a noise-free
low-resolution space. Then the intermediate image is up-sampled with a
pre-trained deep model. Finally, we fine-tune the two modules in an end-to-end
manner to get the high-resolution output. Experiments on NTIRE2018 datasets
demonstrate that the proposed unsupervised method achieves comparable results
as the state-of-the-art supervised models.
