  Recently, single image super-resolution (SR) under large scaling factors has
witnessed impressive progress by introducing pre-trained generative adversarial
networks (GANs) as priors. However, most GAN-Priors based SR methods are
constrained by an attribute disentanglement problem in inverted latent codes
which directly leads to mismatches of visual attributes in the generator layers
and further degraded reconstruction. In addition, stochastic noises fed to the
generator are employed for unconditional detail generation, which tends to
produce unfaithful details that compromise the fidelity of the generated SR
image. We design LAREN, a LAtent multi-Relation rEasoNing technique that
achieves superb large-factor SR through graph-based multi-relation reasoning in
latent space. LAREN consists of two innovative designs. The first is
graph-based disentanglement that constructs a superior disentangled latent
space via hierarchical multi-relation reasoning. The second is graph-based code
generation that produces image-specific codes progressively via recursive
relation reasoning which enables prior GANs to generate desirable image
details. Extensive experiments show that LAREN achieves superior large-factor
image SR and outperforms the state-of-the-art consistently across multiple
benchmarks.
