Noise Conditional Flow Model for Learning the Super-Resolution Space
This addresses the problem of limited diversity in super-resolution outputs for image processing applications, though it is incremental as it builds on prior flow-based methods like SRFlow.
The paper tackles the ill-posed nature of super-resolution by proposing NCSR, a noise conditional flow model that improves diversity and visual quality in generated high-resolution images, achieving better scores than baselines and outperforming in the NTIRE 2021 challenge.
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.