A Fully Progressive Approach to Single-Image Super-Resolution

CVPRW 2018
Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers

A Fully Progressive Approach to Single-Image Super-Resolution

Examples of our 4x and 8x upsampling results. Our model without GAN sets a new state-of-the-art benchmark in terms of PSNR/SSIM; our GAN-extended model yields high perceptual quality and is able to hallucinate plausible details up to 8x upsampling ratio.


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., 8×) 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. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster.


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