  Fundamentally, super-resolution is ill-posed problem because a low-resolution
image can be obtained from many high-resolution images. Recent studies for
super-resolution cannot create diverse super-resolution images. Although SRFlow
tried to account for ill-posed nature of the super-resolution by predicting
multiple high-resolution images given a low-resolution image, there is room to
improve the diversity and visual quality. In this paper, we propose Noise
Conditional flow model for Super-Resolution, NCSR, which increases the visual
quality and diversity of images through noise conditional layer. To learn more
diverse data distribution, we add noise to training data. However, low-quality
images are resulted from adding noise. We propose the noise conditional layer
to overcome this phenomenon. The noise conditional layer makes our model
generate more diverse images with higher visual quality than other works.
Furthermore, we show that this layer can overcome data distribution mismatch, a
problem that arises in normalizing flow models. With these benefits, NCSR
outperforms baseline in diversity and visual quality and achieves better visual
quality than traditional GAN-based models. We also get outperformed scores at
NTIRE 2021 challenge.
