Towards Practical Training Framework for Image Super-resolution

抄録

This article is intended to provide an explanatory review on the paper, “Unpaired Image Super-Resolution using Pseudo-Supervision,” which we published in CVPR 2020 (IEEE/CVF Conference on Computer Vision and Pattern Recognition).

In most studies on learning-based image super-resolution (SR), the paired training dataset is created by downscaling high-resolution (HR) images with a predetermined operation (e.g., bicubic). However, these methods fail to super-resolve real-world low-resolution (LR) images, for which the degradation process is much more complicated and unknown. In the paper, we propose an unpaired SR method using a generative adversarial network that does not require a paired/aligned training dataset.

In this article, we classify existing single image super-resolution methods in terms of training datasets, show how the proposed method resolves their shortcomings, and discuss potential applications to real-world use-case to address industrial demands.