  Typical methods for blind image super-resolution (SR) focus on dealing with
unknown degradations by directly estimating them or learning the degradation
representations in a latent space. A potential limitation of these methods is
that they assume the unknown degradations can be simulated by the integration
of various handcrafted degradations (e.g., bicubic downsampling), which is not
necessarily true. The real-world degradations can be beyond the simulation
scope by the handcrafted degradations, which are referred to as novel
degradations. In this work, we propose to learn a latent representation space
for degradations, which can be generalized from handcrafted (base) degradations
to novel degradations. The obtained representations for a novel degradation in
this latent space are then leveraged to generate degraded images consistent
with the novel degradation to compose paired training data for SR model.
Furthermore, we perform variational inference to match the posterior of
degradations in latent representation space with a prior distribution (e.g.,
Gaussian distribution). Consequently, we are able to sample more high-quality
representations for a novel degradation to augment the training data for SR
model. We conduct extensive experiments on both synthetic and real-world
datasets to validate the effectiveness and advantages of our method for blind
super-resolution with novel degradations.
