  Recently, it has been demonstrated that deep neural networks can
significantly improve the performance of single image super-resolution (SISR).
Numerous studies have concentrated on raising the quantitative quality of
super-resolved (SR) images. However, these methods that target PSNR
maximization usually produce blurred images at large upscaling factor. The
introduction of generative adversarial networks (GANs) can mitigate this issue
and show impressive results with synthetic high-frequency textures.
Nevertheless, these GAN-based approaches always have a tendency to add fake
textures and even artifacts to make the SR image of visually higher-resolution.
In this paper, we propose a novel perceptual image super-resolution method that
progressively generates visually high-quality results by constructing a
stage-wise network. Specifically, the first phase concentrates on minimizing
pixel-wise error, and the second stage utilizes the features extracted by the
previous stage to pursue results with better structural retention. The final
stage employs fine structure features distilled by the second phase to produce
more realistic results. In this way, we can maintain the pixel, and structural
level information in the perceptual image as much as possible. It is useful to
note that the proposed method can build three types of images in a feed-forward
process. Also, we explore a new generator that adopts multi-scale hierarchical
features fusion. Extensive experiments on benchmark datasets show that our
approach is superior to the state-of-the-art methods. Code is available at
https://github.com/Zheng222/PPON.
