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 this paper, we propose an
unpaired SR method using a generative adversarial network that does not require
a paired/aligned training dataset. Our network consists of an unpaired
kernel/noise correction network and a pseudo-paired SR network. The correction
network removes noise and adjusts the kernel of the inputted LR image; then,
the corrected clean LR image is upscaled by the SR network. In the training
phase, the correction network also produces a pseudo-clean LR image from the
inputted HR image, and then a mapping from the pseudo-clean LR image to the
inputted HR image is learned by the SR network in a paired manner. Because our
SR network is independent of the correction network, well-studied existing
network architectures and pixel-wise loss functions can be integrated with the
proposed framework. Experiments on diverse datasets show that the proposed
method is superior to existing solutions to the unpaired SR problem.

本文提出了一种基于生成式对抗网络的无配对图像超分辨率方法，使用一个不需要配对 / 对齐的训练数据集的 unpaired kernel/noise correction 网络和一个伪成对 SR 网络来进行无配对 SR，该方法不仅能够准确去噪并调整 LR 图像的内核，而且还能够生成精确的 HR 图像。