Unsupervised Image Super-Resolution with an Indirect Supervised Path
Shuaijun Chen, Zhen Han, Enyan Dai, Xu Jia, Ziluan Liu, Xing Liu, Xueyi Zou, Chunjing Xu, Jianzhuang Liu, Qi Tian
Abstract
The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low- resolution (LR) image. Although significant progress has been made with deep learning models, they are trained on synthetic paired data in a supervised way and do not perform well on real cases. There are several attempts that directly apply unsupervised image translation models to address such a problem. However, unsupervised image translation models need to be modified to adapt to unsupervised low-level vision task which poses higher requirement on the accuracy of translation. In this work, we propose a novel framework which is composed of two stages: 1) unsupervised image translation between real LR and synthetic LR images; 2) supervised super-resolution from approximated real LR images to the paired HR images. It takes the synthetic LR images as a bridge and creates an indirect supervised path. We show that our framework is so flexible that any unsupervised translation model and deep learning based super-resolution model can be integrated into it. Besides, a collaborative training strategy is proposed to encourage the two stages collaborate with each other for better degradation learning and super-resolution performance. The proposed method achieves very good performance on datasets of NTIRE 2017, NTIRE 2018 and NTIRE 2020, even comparable with supervised methods.