  Real-world face super-resolution (SR) is a highly ill-posed image restoration
task. The fully-cycled Cycle-GAN architecture is widely employed to achieve
promising performance on face SR, but prone to produce artifacts upon
challenging cases in real-world scenarios, since joint participation in the
same degradation branch will impact final performance due to huge domain gap
between real-world and synthetic LR ones obtained by generators. To better
exploit the powerful generative capability of GAN for real-world face SR, in
this paper, we establish two independent degradation branches in the forward
and backward cycle-consistent reconstruction processes, respectively, while the
two processes share the same restoration branch. Our Semi-Cycled Generative
Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the
domain gap between the real-world LR face images and the synthetic LR ones, and
to achieve accurate and robust face SR performance by the shared restoration
branch regularized by both the forward and backward cycle-consistent learning
processes. Experiments on two synthetic and two real-world datasets demonstrate
that, our SCGAN outperforms the state-of-the-art methods on recovering the face
structures/details and quantitative metrics for real-world face SR. The code
will be publicly released at https://github.com/HaoHou-98/SCGAN.
