  We describe our solution for the PIRM Super-Resolution Challenge 2018 where
we achieved the 2nd best perceptual quality for average RMSE<=16, 5th best for
RMSE<=12.5, and 7th best for RMSE<=11.5. We modify a recently proposed
Multi-Grid Back-Projection (MGBP) architecture to work as a generative system
with an input parameter that can control the amount of artificial details in
the output. We propose a discriminator for adversarial training with the
following novel properties: it is multi-scale that resembles a progressive-GAN;
it is recursive that balances the architecture of the generator; and it
includes a new layer to capture significant statistics of natural images.
Finally, we propose a training strategy that avoids conflicts between
reconstruction and perceptual losses. Our configuration uses only 281k
parameters and upscales each image of the competition in 0.2s in average.
