  We show that pre-trained Generative Adversarial Networks (GANs), e.g.,
StyleGAN, can be used as a latent bank to improve the restoration quality of
large-factor image super-resolution (SR). While most existing SR approaches
attempt to generate realistic textures through learning with adversarial loss,
our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by
directly leveraging rich and diverse priors encapsulated in a pre-trained GAN.
But unlike prevalent GAN inversion methods that require expensive
image-specific optimization at runtime, our approach only needs a single
forward pass to generate the upscaled image. GLEAN can be easily incorporated
in a simple encoder-bank-decoder architecture with multi-resolution skip
connections. Switching the bank allows the method to deal with images from
diverse categories, e.g., cat, building, human face, and car. Images upscaled
by GLEAN show clear improvements in terms of fidelity and texture faithfulness
in comparison to existing methods.
