  Super-resolution aims at increasing the resolution and level of detail within
an image. The current state of the art in general single-image super-resolution
is held by NESRGAN+, which injects a Gaussian noise after each residual layer
at training time. In this paper, we harness evolutionary methods to improve
NESRGAN+ by optimizing the noise injection at inference time. More precisely,
we use Diagonal CMA to optimize the injected noise according to a novel
criterion combining quality assessment and realism. Our results are validated
by the PIRM perceptual score and a human study. Our method outperforms NESRGAN+
on several standard super-resolution datasets. More generally, our approach can
be used to optimize any method based on noise injection.
