  Deep Convolutional Neural Networks (DCNNs) have exhibited impressive
performance on image super-resolution tasks. However, these deep learning-based
super-resolution methods perform poorly in real-world super-resolution tasks,
where the paired high-resolution and low-resolution images are unavailable and
the low-resolution images are degraded by complicated and unknown kernels. To
break these limitations, we propose the Unsupervised Bi-directional Cycle
Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN),
which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network
(UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN).
First, the UBCDTN is able to produce an approximated real-like LR image through
transferring the LR image from an artificially degraded domain to the
real-world LR image domain. Second, the SESRN has the ability to super-resolve
the approximated real-like LR image to a photo-realistic HR image. Extensive
experiments on unpaired real-world image benchmark datasets demonstrate that
the proposed method achieves superior performance compared to state-of-the-art
methods.
