1 research outputs found
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
These days, unsupervised super-resolution (SR) has been soaring due to its
practical and promising potential in real scenarios. The philosophy of
off-the-shelf approaches lies in the augmentation of unpaired data, i.e. first
generating synthetic low-resolution (LR) images corresponding
to real-world high-resolution (HR) images in the real-world LR
domain , and then utilizing the pseudo pairs for training in a supervised manner. Unfortunately, since
image translation itself is an extremely challenging task, the SR performance
of these approaches are severely limited by the domain gap between generated
synthetic LR images and real LR images. In this paper, we propose a novel
domain-distance aware super-resolution (DASR) approach for unsupervised
real-world image SR. The domain gap between training data (e.g.
) and testing data (e.g. ) is addressed with our
\textbf{domain-gap aware training} and \textbf{domain-distance weighted
supervision} strategies. Domain-gap aware training takes additional benefit
from real data in the target domain while domain-distance weighted supervision
brings forward the more rational use of labeled source domain data. The
proposed method is validated on synthetic and real datasets and the
experimental results show that DASR consistently outperforms state-of-the-art
unsupervised SR approaches in generating SR outputs with more realistic and
natural textures.Comment: Code will be available at https://github.com/ShuhangGu/DAS