Recent deep learning based single image super-resolution (SISR) methods
mostly train their models in a clean data domain where the low-resolution (LR)
and the high-resolution (HR) images come from noise-free settings (same domain)
due to the bicubic down-sampling assumption. However, such degradation process
is not available in real-world settings. We consider a deep cyclic network
structure to maintain the domain consistency between the LR and HR data
distributions, which is inspired by the recent success of CycleGAN in the
image-to-image translation applications. We propose the Super-Resolution
Residual Cyclic Generative Adversarial Network (SRResCycGAN) by training with a
generative adversarial network (GAN) framework for the LR to HR domain
translation in an end-to-end manner. We demonstrate our proposed approach in
the quantitative and qualitative experiments that generalize well to the real
image super-resolution and it is easy to deploy for the mobile/embedded
devices. In addition, our SR results on the AIM 2020 Real Image SR Challenge
datasets demonstrate that the proposed SR approach achieves comparable results
as the other state-of-art methods.

本研究提出了一种基于 CycleGAN 和 GAN 框架的超分辨率方法，能够在真实世界的低分辨率与高分辨率图像转换中，保持数据分布的连续性，表现出较高的超分辨率效果。