  Deep Convolution Neural Networks (CNN) have achieved significant performance
on single image super-resolution (SR) recently. However, existing CNN-based
methods use artificially synthetic low-resolution (LR) and high-resolution (HR)
image pairs to train networks, which cannot handle real-world cases since the
degradation from HR to LR is much more complex than manually designed. To solve
this problem, we propose a real-world LR images guided bi-cycle network for
single image super-resolution, in which the bidirectional structural
consistency is exploited to train both the degradation and SR reconstruction
networks in an unsupervised way. Specifically, we propose a degradation network
to model the real-world degradation process from HR to LR via generative
adversarial networks, and these generated realistic LR images paired with
real-world HR images are exploited for training the SR reconstruction network,
forming the first cycle. Then in the second reverse cycle, consistency of
real-world LR images are exploited to further stabilize the training of SR
reconstruction and degradation networks. Extensive experiments on both
synthetic and real-world images demonstrate that the proposed algorithm
performs favorably against state-of-the-art single image SR methods.
