Multi--Scale Recursive and Perception--Distortion Controllable Image Super--Resolution

Pablo Navarrete Michelini, Dan Zhu, Hanwen Liu; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

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.