  Recent deep learning approaches to single image super-resolution have
achieved impressive results in terms of traditional error measures and
perceptual quality. However, in each case it remains challenging to achieve
high quality results for large upsampling factors. To this end, we propose a
method (ProSR) that is progressive both in architecture and training: the
network upsamples an image in intermediate steps, while the learning process is
organized from easy to hard, as is done in curriculum learning. To obtain more
photorealistic results, we design a generative adversarial network (GAN), named
ProGanSR, that follows the same progressive multi-scale design principle. This
not only allows to scale well to high upsampling factors (e.g., 8x) but
constitutes a principled multi-scale approach that increases the reconstruction
quality for all upsampling factors simultaneously. In particular ProSR ranks
2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge
[34]. Compared to the top-ranking team, our model is marginally lower, but runs
5 times faster.
