Noise Conditional Flow Model for Learning the Super-Resolution Space

TL;DR

This paper introduces NCSR, a noise conditional flow model for super-resolution that enhances diversity and visual quality of generated images by incorporating a noise conditional layer, outperforming previous methods including GANs.

Contribution

The paper proposes a novel noise conditional layer in flow-based super-resolution models to improve image diversity and quality, addressing data distribution mismatch.

Findings

NCSR outperforms baseline models in diversity and visual quality.

NCSR achieves better visual quality than traditional GAN-based models.

NCSR outperforms other methods at NTIRE 2021 challenge.

Abstract

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.